LearnQuest Courses

June 28, 2022 2023-02-22 11:02

Apple	Dell	Google	IBM
Apple Training and Certification	Dell Boomi	Google Cloud	Business Analysis
App Development	Dell Boomi	Application Development	Cloudera – Data Analytics and Cloud
Creative		Cloud Platform Architect	Linux
End User		Data & Machine Learning	Administration Linux
macOS		Exam Prep	Power Systems Linux
Productivity		Your Gateway to Google Cloud Platform	Programming Linux
		Google-Analytics	Linux Foundation
		Google-Application Development	Administration
		Google-Data and Machine Learning	Automation
		google-Data and Machine Learning-Data Engineering	Mirantis – OpenStack
		Looker	Miscellaneous – IBM
		Google-Your Gateway to Google Cloud Platform-Business	Other Programming Languages – RPG

1.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-100-IN		Course Name: Google Cloud Platform Fundamentals: Core Infrastructure
	Description:&nbsp: This Google Cloud Platform Fundamentals Core Infrastructure course is designed to provide students with an overview of Google Cloud Platform products and services. Through a combination of presentations, demos, and hands-on labs, participants learn the value of Google Cloud Platform and how to incorporate cloud-based solutions into business strategies. Students learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, and Google Container Engine.
	Objectives: The Google Cloud Platform Fundamentals Core Infrastructure course teaches participants the following skills: Identify the purpose and value of Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Container Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics
	Audience: The Google Cloud Platform Fundamentals Core Infrastructure course is intended for: Core Infrastructure provides a first look at Google Cloud Platform for technical learners who are not already familiar with a public cloud.
	Pre Requisites: Familiarity with application development, systems operations, Linux operating systems, and data analytics/machine learning is helpful in understanding the technologies covered.
	Duration: 1
	Topics: Introducing Google Cloud Platform Google Cloud Platform offers four main kinds of services: Compute, Storage, Big Data, and Machine Learning. This course focuses mostly on the first two, together with Google Virtual Private Cloud (VPC) networking. This module orients learners to the basics of Google Cloud Platform. It traces the evolution of cloud computing and explains what is unique about Google’s approach to it. The module introduces the key structural concepts of regions and zones. Getting Started with Google Cloud Platform GCP customers use projects to organize the resources they use. They use Google Cloud Identity and Access Management, also called IAM, to control who can do what with those resources. They use any of several technologies to connect with GCP. This module covers each of these topics, and it introduces a service called Cloud Launcher that is an easy way to get started with GCP. Virtual Machines in the Cloud Compute Engine lets you run virtual machines on Google’s global infrastructure. This module covers how Compute Engine works, with a focus on Google virtual networking. Storage in the Cloud Every application needs to store data. Different applications and workloads require different storage and database solutions. This module describes and differentiates among GCP’s core storage options: Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Datastore, and Google Bigtable. Containers in the Cloud Containers are simple and interoperable, and they enable seamless, fine-grained scaling. Kubernetes is an orchestration layer for containers. Kubernetes Engine is Kubernetes as a service, a scalable managed offering that runs on Google’s infrastructure. You direct the creation of a cluster, and Kubernetes Engine schedules your containers into the cluster and manages them automatically, based on requirements you define. This module explains how Kubernetes Engine works and how it helps deploy applications in containers. Applications in the Cloud App Engine is a Platform-as-a-Service (‘PaaS’) offering. The App Engine platform manages the hardware and networking infrastructure required to run your code. App Engine provides built-in services that many web applications need. This module describes how App Engine works. Developing, Deploying and Monitoring in the Cloud Popular tools for development, deployment, and monitoring just work in GCP. Customers also have options for tools in each of these three areas that are tightly integrated with GCP. This module covers those tools. Big Data and Machine Learning in the Cloud GCP’s big-data and machine learning offerings are intended to help customers get the most out of data. These tools are intended to be simple and practical to embed in your applications. This module describes the available big-data and machine learning services and explains the usefulness of each. Summary and Review This module reviews the GCP services covered in this course and reminds learners of the differences among them. The module compares GCP compute services, GCP storage services, and important Google VPC networking capabilities.

2.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-110-IN		Course Name: Preparing for the Professional Cloud Architect Examination
	Description:&nbsp: Students in this course will get a basic overview of the Professional Cloud Architect exam, review the sample case studies, and review each section of the exam. Students will be able to identify skill gaps and further areas of study and be pointed to appropriate target learning resources.
	Objectives:
	Audience: Cloud professionals who intend to take the Professional Cloud Architect certification exam.
	Pre Requisites: Knowledge and experience with GCP, equivalent to GCP Architecting Infrastructure Knowledge of cloud solutions, equivalent to GCP Design and Process Industry experience with cloud computing
	Duration: 1
	Topics: Understanding the Professional Cloud Architect CertificationEstablish basic knowledge about the certification exam and eliminate any confusion or misunderstandings about the process and nature of the exam itself. Position the Professional Cloud Architect certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Cloud Architect and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case StudiesIn-depth review of the Case Studies provided for exam preparation. JencoMart MountKirk Games Dress4Win TerramEarth Designing and ImplementingTips and examples covering design and implementation skills that could be tested on the exam Review the layered model from Design and Process Provide exam tips focused on business and technical design Designing a solution infrastructure that meets business requirements Designing a solution infrastructure that meets technical requirements Design network, storage, and compute resources Creating a migration plan Envisioning future solution improvements Resources for learning more about designing and planning Configuring network topologies Configuring individual storage systems Configuring compute systems Resources for learning more about managing and provisioning Designing for security Designing for legal compliance Resources for learning more about security and compliance Optimizing and OperatingTips and examples covering business processes, technical, optimization for security, performance, cost, and ongoing operations and reliability Analyzing and defining technical processes Analyzing and defining business processes Resources for learning more about analyzing and optimizing processes Designing for security Designing for legal compliance Resources for learning more about security and compliance Advising development/operation teams to ensure successful deployment of the solution Resources for learning more about managing implementation Easy buttons Playbooks Developing a resilient culture Resources for learning more about ensuring reliability Next StepsHighlght learning resources Present Qwiklabs Challenge Quest for the Professional CA Identify Instructor Led Training courses and what they cover that will be helpful based on skills that might be on the exam Connect candidates to individual Qwiklabs, and to Coursera individual courses and specializations. Review/feedback of course

3.	Company: Google	Category: Google Cloud		Sub Category: Exam Prep
	Course No.: GCP-111-IN		Course Name: Preparing for the Associate Cloud Engineer Examination
	Description:&nbsp: This one-day instructor-led course helps prospective candidates structure their preparation for the Associate Cloud Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, demos and hands-on labs, candidates will familiarize themselves with the domains covered by the examination. An Associate Cloud Engineer deploys applications, monitors operations, and manages enterprise solutions. With a shortage of cloud expertise in the job market, one which is projected to last for the next several years, Google Cloud certifications can be a way to differentiate yourself from the rest and prove you have not only the technical knowledge, but the skills required to do the job. This course by itself will not prepare a candidate to pass the Associate Cloud Engineer certification exam. It will, however, help the candidate better understand the areas covered by the exam and navigate the recommended resources provided by Google and Qwiklabs for preparing to take the exam, so they can formulate their own personal study plan.
	Objectives:
	Audience: Cloud professionals who intend to take the Professional Cloud Engineer Examination
	Pre Requisites: Recommended experience: 6 months+ hands-on experience with GCP
	Duration: 1
	Topics:

4.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-115-IN		Course Name: Networking in Google Cloud Platform
	Description:&nbsp: This two-day instructor-led Networking in Google Cloud Platform class gives participants broad study of networking options on Google Cloud. Through a combination of presentations, demonstrations, and hands-on labs, participants explore and deploy Google Cloud networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN; Cloud NAT. The course will also cover common network design patterns and automated deployment using Deployment Manager or Terraform.
	Objectives: Upon completion of the Networking in Google Cloud Platform course, students will be able to: Configure Google VPC networks, subnets, and routers Control administrative access to VPC objects Control network access to endpoints in VPCs Interconnect networks among GCP projects Interconnect networks among GCP VPC networks and on-premises or other-cloud networks Choose among GCP load balancer and proxy options and configure them Use Cloud CDN to reduce latency and save money Optimize network spend using Network Tiers Deploy networks declaratively using Cloud Deployment Manager Design networks to meet common customer requirements Configure monitoring and logging to troubleshoot networks problems
	Audience: This class is intended for network engineers and network admins that are either using Google Cloud or are planning to do so. The class is also for individuals that want to be exposed to software-defined networking solutions in the cloud.
	Pre Requisites: Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience Clear understanding of the 7-layer OSI model Clear understanding of IPv4 addressing Prior experience with managing IPv4 routes
	Duration: 2
	Topics: Google Cloud VPC Networking Fundamentals Recall that networks belong to projects Explain the differences among default, auto, and custom networks Create networks and subnets Explain how IPv4 addresses are assigned to Compute Engine instances Publish domain names using Google Cloud DNS Create Compute Engine instances with IP aliases Create Compute Engine instances with multiple virtual network Controlling Access to VPC Networks Outline how IAM policies affect VPC networks Control access to network resources using service accounts Control access to Compute Engine instances with tag-based firewall rules Sharing Networks across Projects Outline the overall workflow for configuring shared VPC Differentiate between the IAM roles that allow network resources to be managed Configure peering between unrelated VPC networks Recall when to use shared VPC and when to use VPC peering Load Balancing Recall the various load balancing services Configure Layer 7 HTTP(S) load balancing Whitelist and blacklist IP traffic with Cloud Armor Cache content with Cloud CDN Explain Layer 4 TCP or SSL proxy load balancing. Explain regional network load balancing Configure internal load balancing Recall the choices for enabling IPv6 Internet connectivity for Google Cloud load balancers Determine which Google Cloud load balancer to use when Hybrid Connectivity Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Explain Dedicated Interconnect and Partner Interconnect Describe the workflow for configuring a Dedicated Interconnect Build a connection over a VPN with Cloud Router Determine which GCP interconnect service to use when Explain Direct Peering and Partner Peering Determine which GCP peering service to use when Explain Direct Peering and Partner Peering Determine which Google Cloud peering service to use when Networking Pricing and Billing Recognize how networking features are charged for Use Network Service Tiers to optimize spend Determine which Network Service Tier to use when Recall that labels can be used to understand networking spend Network Design and Deployment Explain common network design patterns Configure Private Google Access to allow access to certain Google Cloud services from VM instances with only internal IP addresses. Configure Cloud NAT to provide your instances without public IP addresses access to the internet Automate the deployment of networks using Deployment Manager or Terraform Launch networking solutions using Cloud Marketplace Network Monitoring and Troubleshooting Configure uptime checks, alerting policies, and charts for your network services Use VPC Flow Logs to log and analyze network traffic behavior

5.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-126-IN		Course Name: Security in Google Cloud Platform
	Description:&nbsp: This course gives students broad study of security controls and techniques on Google Cloud Platform. Through lectures, demonstrations, and hands-on labs, students explore and deploy the components of a secure Google Cloud solution. Students also learn mitigation techniques for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use.
	Objectives: Upon completion of the Security in Google Cloud Platform course, students will be able to: Understanding the Google approach to security Managing administrative identities using Cloud Identity Implementing least privilege administrative access using Google Cloud Resource Manager, Cloud IAM Implementing IP traffic controls using VPC firewalls and Cloud Armor Implementing Identity Aware Proxy Analyzing changes to the configuration or metadata of resources with GCP audit logs Scanning for and redact sensitive data with the Data Loss Prevention API Scanning a GCP deployment with Forseti Remediating important types of vulnerabilities, especially in public access to data and VMs
	Audience: This class is intended for the following job roles: [Cloud] information security analysts, architects, and engineers Information security/cybersecurity specialists Cloud infrastructure architects Additionally, the course is intended for Google and partner field personnel who work with customers in those job roles. The course should also be useful to developers of cloud applications.
	Pre Requisites: Prior completion of Google Cloud Platform Fundamentals: Core Infrastructure or equivalent experience Prior completion of Networking in Google Cloud Platform or equivalent experience Knowledge of foundational concepts in information security: Fundamental concepts: vulnerability, threat, attack surface, confidentiality, integrity, availability Common threat types and their mitigation strategies Public-key cryptography: Public and private key pairs, Certificates, Cipher types, Key width Certificate authorities Transport Layer Security/Secure Sockets Layer encrypted communication Public key infrastructures Security policy Basic proficiency with command-line tools and Linux operating system environments Systems Operations experience, including deploying and managing applications, either on-premises or in a public cloud environment Reading comprehension of code in Python or JavaScript
	Duration: 3
	Topics: PART I: Managing Security in Google Cloud Platform Foundations of GCP Security Google Cloud’s approach to security The shared security responsibility model Threats mitigated by Google and by GCP Access Transparency Cloud Identity Cloud Identity Syncing with Microsoft Active Directory Choosing between Google authentication and SAML-based SSO GCP best practices Identity and Access Management GCP Resource Manager: projects, folders, and organizations GCP IAM roles, including custom roles GCP IAM policies, including organization policies GCP IAM best practices Configuring Google Virtual Private Cloud for Isolation and Security Configuring VPC firewalls (both ingress and egress rules) Load balancing and SSL policies Private Google API access SSL proxy use Best practices for structuring VPC networks Best security practices for VPNs Security considerations for interconnect and peering options Available security products from partners Monitoring, Logging, Auditing, and Scanning Stackdriver monitoring and logging VPC flow logs Cloud audit logging Deploying and Using Forseti Securing Compute Engine: techniques and best practices Compute Engine service accounts, default and customer-defined IAM roles for VMs API scopes for VMs Managing SSH keys for Linux VMs Managing RDP logins for Windows VMs Organization policy controls: trusted images, public IP address, disabling serial port Encrypting VM images with customer-managed encryption keys and with customer-supplied encryption keys Finding and remediating public access to VMs VM best practices Encrypting VM disks with customer-supplied encryption keys Securing cloud data: techniques and best practices Cloud Storage and IAM permissions Cloud Storage and ACLs Auditing cloud data, including finding and remediating publicly accessible data Signed Cloud Storage URLs Signed policy documents Encrypting Cloud Storage objects with customer-managed encryption keys and with customer-supplied encryption keys Best practices, including deleting archived versions of objects after key rotation BigQuery authorized views BigQuery IAM roles Best practices, including preferring IAM permissions over ACLs Protecting against Distributed Denial of Service Attacks: techniques and best practices How DDoS attacks work Mitigations: GCLB, Cloud CDN, autoscaling, VPC ingress and egress firewalls, Cloud Armor Types of complementary partner products Application Security: techniques and best practices Types of application security vulnerabilities DoS protections in App Engine and Cloud Functions Cloud Security Scanner Threat: Identity and Oauth phishing Identity Aware Proxy Content-related vulnerabilities: techniques and best practices Threat: Ransomware Mitigations: Backups, IAM, Data Loss Prevention API Threats: Data misuse, privacy violations, sensitive/restricted/unacceptable content Mitigations: Classifying content using Cloud ML APIs; scanning and redacting data using Data Loss Prevention API

6.	Company: Google	Category: Google Cloud		Sub Category: Application Development
	Course No.: GCP-140-IN		Course Name: Developing Applications with Google Cloud Platform
	Description:&nbsp: In this Developing Applications with Google Cloud Platform course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
	Objectives: Upon completion of the Developing Applications with Google Cloud Platform course, students will be able to: Use best practices for application development. Choose the appropriate data storage option for application data. Implement federated identity management. Develop loosely coupled application components or microservices. Integrate application components and data sources. Debug, trace, and monitor applications. Perform repeatable deployments with containers and deployment services. Choose the appropriate application runtime environment; use Google Kubernetes Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex.
	Audience: Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform
	Pre Requisites: Completed Google Cloud Platform Fundamentals or have equivalent experience Working knowledge of Node.js Basic proficiency with command-line tools and Linux operating system environments
	Duration: 3
	Topics: Best Practices for Application Development Code and environment management Design and development of secure, scalable, reliable, loosely coupled application components and microservices Continuous integration and delivery Re-architecting applications for the cloud Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials Overview of Data Storage Options Overview of options to store application data Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner Best Practices for Using Google Cloud Datastore Best practices related to the following: Queries Built-in and composite indexes Inserting and deleting data (batch operations) Transactions Error handling Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow Lab: Store application data in Cloud Datastore Performing Operations on Buckets and Objects Operations that can be performed on buckets and objects Consistency model Error handling Best Practices for Using Google Cloud Storage Naming buckets for static websites and other uses Naming objects (from an access distribution perspective) Performance considerations Setting up and debugging a CORS configuration on a bucket Lab: Store files in Cloud Storage Handling Authentication and Authorization Cloud Identity and Access Management (IAM) roles and service accounts User authentication by using Firebase Authentication User authentication and authorization by using Cloud Identity-Aware Proxy Lab: Authenticate users by using Firebase Authentication Using Google Cloud Pub/Sub to Integrate Components of Your Application Topics, publishers, and subscribers Pull and push subscriptions Use cases for Cloud Pub/Sub Lab: Develop a backend service to process messages in a message queue Adding Intelligence to Your Application Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API Using Google Cloud Functions for Event-Driven Processing Key concepts such as triggers, background functions, HTTP functions Use cases Developing and deploying functions Logging, error reporting, and monitoring Managing APIs with Google Cloud Endpoints Open API deployment configuration Lab: Deploy an API for your application Deploying an Application by Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager Creating and storing container images Repeatable deployments with deployment configuration and templates Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments Execution Environments for Your Application Considerations for choosing an execution environment for your application or service: Google Compute Engine Kubernetes Engine App Engine flexible environment Cloud Functions Cloud Dataflow Lab: Deploying your application on App Engine flexible environment Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver Stackdriver Debugger Stackdriver Error Reporting Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting Stackdriver Logging Key concepts related to Stackdriver Trace and Stackdriver Monitoring. Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance

7.	Company: Google	Category: Google-Data and Machine Learning		Sub Category: Looker
	Course No.: GCP-1680-IN		Course Name: Analyzing and Visualizing Data with Looker
	Description:&nbsp: This Analyzing and Visualizing Data with Looker course is an introductory-level training that outlines Looker’s capabilities for working with data and provides guided demos and hands-on practice with Looker functionality for data exploration, analysis and visualization.
	Objectives: Upon completion of the Analyzing and Visualizing Data with Lookercourse, students will be able to: Define Looker and the capabilities it provides for working with data Explain the four core analytical concepts in Looker (dimensions, measures, filters, pivots) Use dimensions, measures, filters, and pivots to analyze and visualize data Create advanced metrics instantaneously with table calculations Create dashboards to combine and share visualizations Utilize folders and boards in Looker to organize content for navigability and discoverability
	Audience: Business users who need to draw insights from data; Data analysts who are responsible for data analysis and visualization within their organizations.
	Pre Requisites: None
	Duration: 1
	Topics:

8.	Company: Google	Category: Google-Data and Machine Learning		Sub Category: Looker
	Course No.: GCP-1708-IN		Course Name: Developing Data Models with LookML
	Description:&nbsp: This Developing Data Models with LookML course is an intermediate-level training that introduces the fundamentals of LookML for Looker developers and provides guided demos and hands-on practice with writing LookML code.
	Objectives: Upon completion of the Developing Data Models with LookML course, students will be able to: Define LookML basic terms and building blocks Use the Looker Integrated Development Environment (IDE) and project version control to modify LookML projects Create dimensions and measures to curate data attributes used by business users Create and design Explores to make data accessible to business users Use derived tables to instantaneously create new tables Use caching and datagroups in Looker to speed up SQL queries
	Audience: Data developers who are responsible for data curation and management within their organizations; Data analysts interested in learning how data developers use LookML to curate and manage data in their organization’s Looker instance.
	Pre Requisites: Basic understanding of SQL, Git, and the Looker business user experience. For learners with no previous experience as data explorers in Looker, it is recommended to first complete Analyzing and Visualizing Data in Looker.
	Duration: 1
	Topics:

9.	Company: Google	Category: Google-Application Development		Sub Category: google-Application Development-Application Development
	Course No.: GCP-1709-IN		Course Name: Application Development with Cloud Run
	Description:&nbsp: This course introduces students to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, students will learn how to design, implement, deploy, secure, manage, and scale new (greenfield) and existing (brownfield) applications on Google Cloud using Cloud Run.
	Objectives: After completing the Application Development with Cloud Run course, students will be able to: Gain detailed understanding of Cloud Run, Google Cloud’s fully managed compute platform for deploying and scaling containerized applications quickly and securely. Write and migrate code your way using your favorite languages (Go, Python, Java, Ruby, Node.js, and more). Secure service to service communication based on service identities and grant applications only the permissions they need. Learn how to build highly available applications with low end-user latency, globally. Learn how to connect to, and persist data in the managed database offerings on Google Cloud. Understand how abstracting away all infrastructure management creates a simple developer experience.
	Audience: Cloud Developers, API Developers
	Pre Requisites: Familiarity with Linux commands and command line interface. Basic understanding of Google Cloud. Basic understanding of networking. Basic understanding of one or more programming languages like Go, Python, Java, Ruby, or Node.js. Basic understanding of shell scripts, YAML, JSON, HTTP, and TLS.
	Duration: 3
	Topics: The course includes presentations, demonstrations, and hands-on labs.Module 1: Introducing Application Development with Cloud Run A general understanding of Cloud Run. Understand how how high availability, low end-user latency and developer productivity are important architectural drivers for web based applications today. Understand the advantages of serverless on Google Cloud. Module 2: Understanding Cloud Run Understand Container Images and Containers. Understand how Cloud Run is different from an always-on server. Implement the deployment of a container image to Cloud Run. Understand auto-scaling and on-demand containers. Module 3: Building Container Images Deeply understand what is inside a container image. Package an application into a container image with Buildpacks. Understand that Dockerfiles are a lower-level and more transparent alternative to Buildpacks. Module 4: Building Container Images Understand the advantages of the shutdown lifecycle hook. Understand how to avoid request queuing. Implement new versions of an application. Implement gradual traffic migration. Module 5: Configuring Service Identity and Authorization Understand that every action on a Cloud resource is actually an API call. Understand how and why to limit the permissions in your Cloud Run service to only specific and necessary API calls. Understand the process needed to make the default permissions of a Cloud API more secure. Use the client libraries to call other Google Cloud services. Module 6: Serving Requests Use Cloud CDN to improve the reliability and performance of an application. Use path-based routing to combine multiple applications on one domain. Route incoming requests to the Cloud Run service closest to clients. Module 7: Using Inbound and Outbound Access Control Connecting your project to resources with a private IP. Implementing controls to prevent outbound traffic to dangerous or unwanted hosts. Implementing filters for inbound traffic using content-based rules. Implementing controlled access to only specific service accounts. Implement dialogs using input and output contexts. Module 8: Persisting Data Understand how to connect your application with Cloud SQL to store relational data. Use a VPC Connector to reach a private Memorystore instance. Understand how to connect with Cloud Storage, Spanner and Firestore. Module 9: Implementing Service-to-Service Communication Using Cloud Pub/Sub to send messages between services. Discovering the URL of other Cloud Run services. Receiving events from other Google Cloud services. Processing background tasks asynchronously. Module 10: Orchestrating and Automating Serverless Workflows Understand the capabilities of Cloud Workflows. Learn how to model a simple workflow with steps and conditional jumps. Integrating Cloud Run with Cloud Workflows. Understand how to invoke workflows.

10.	Company: Google	Category: Google Cloud		Sub Category: Data & Machine Learning
	Course No.: GCP-250-IN		Course Name: Data Engineering on Google Cloud Platform
	Description:&nbsp: This Data Engineering on Google Cloud Platform course is designed to provide participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
	Objectives: The Data Engineering on Google Cloud Platform course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data
	Audience: The Data Engineering on Google Cloud Platform course is intended for experienced developers who are responsible for managing big data transformations including: Extracting, Loading, Transforming, cleaning, and validating data Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports
	Pre Requisites: Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics
	Duration: 4
	Topics: Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines, EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks Whats a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

11.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-300-IN		Course Name: Architecting with Google Cloud Platform: Design and Process
	Description:&nbsp: This two-day Architecting with Google Cloud Platform: Design and Process instructor-led course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine course and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to design Google Cloud deployments that are highly reliable and secure; and how to operate Google Cloud deployments in a highly available and cost-effective manner.Through a combination of presentations, demos, and hands-on labs, participants learn to design GCP deployments that are highly reliable and secure; and how to operate GCP deployments in a highly available and cost-effective manner.
	Objectives: Upon completion of the Architecting with Google Cloud Platform: Design and Process course, students will be able to: Apply a tool set of questions, techniques and design considerations Define application requirements and express them objectively as KPIs, SLO’s and SLI’s Decompose application requirements to find the right microservice boundaries Leverage Google Cloud developer tools to set up modern, automated deployment pipelines Choose the appropriate Google Cloud Storage services based on application requirements Architect cloud and hybrid networks Implement reliable, scalable, resilient applications balancing key performance metrics with cost Choose the right Google Cloud deployment services for your applications Secure cloud applications, data and infrastructure Monitor service level objectives and costs using Stackdriver tools
	Audience: Cloud Solutions Architects, Site Reliability Engineers, Systems Operations professionals, DevOps Engineers, IT managers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.
	Pre Requisites: Completion of Architecting with Google Compute Engine, Architecting with Google Kubernetes Engine or equivalent experience Basic proficiency with command-line tools and Linux operating system environments Systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment
	Duration: 2
	Topics: DEFINING THE SERVICE Design in this class. State and solution. Measurement. Gathering requirements, SLOs, SLAs, and SLIs (key performance indicators). BUSINESS-LOGIC LAYER DESIGN Microservices architecture. GCP 12-factor support. Mapping compute needs to Google Cloud Platform processing services. Compute system provisioning. DATA LAYER DESIGN Classifying and characterizing data. Data ingest and data migration. Identification of storage needs and mapping to Google Cloud Platform storage systems. PRESENTATION LAYER DESIGN Network edge configuration. Network configuration for data transfer within the service, including load balancing and network location. Network integration with other environments, including on premise and multi-cloud. DESIGN FOR RESILIENCY, SCALABILITY, AND DISASTER RECOVERY Failure due to loss of resources. Failure due to overload. Strategies for coping with failure. Business continuity and disaster recovery, including restore strategy and data lifecycle management. Scalable and resilient design. DESIGN FOR SECURITY Google Cloud Platform security. Network access control and firewalls. Protections against denial of service. Resource sharing and isolation. Data encryption and key management. Identity access and auditing. CAPACITY PLANNING AND COST OPTIMIZATION Capacity planning. Pricing. DEPLOYMENT, MONITORING AND ALERTING, AND INCIDENT RESPONSE Deployment. Monitoring and alerting. Incident response.

12.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-305-IN		Course Name: Architecting Hybrid Cloud Infrastructure with Anthos
	Description:&nbsp: This two-day instructor-led course prepares students to modernize, manage, and observe their applications using Kubernetes whether the application is deployed on-premises or on Google Cloud Platform (GCP). Through presentations, and hands-on labs, students explore and deploy using Kubernetes Engine (GKE), GKE Connect, Istio service mesh and Anthos Config Management capabilities that enable operators to work with modern applications even when split among multiple clusters hosted by multiple providers, or on-premises.
	Objectives: Upon completion of the Architecting Hybrid Cloud Infrastructure with Anthos course, students will be able to: Connect and manage Anthos GKE clusters from GCP Console whether clusters are part of Anthos on Google Cloud or Anthos deployed on VMware. Understand how service mesh proxies are installed, configured and managed. Configure centralized logging, monitoring, tracing, and service visualizations wherever the Anthos GKE clusters are hosted. Understand and configure fine-grained traffic management. Use service mesh security features for service-service authentication, user authentication, and policy-based service authorization. Install a multi-service application spanning multiple clusters in a hybrid environment. Understand how services communicate across clusters. Migrate services between clusters. Install Anthos Config Management, use it to enforce policies, and explain how it can be used across multiple clusters.
	Audience: This course is primarily intended for the following participants: Technical employees using GCP, including customer companies, partners and system integrators: deployment engineers, cloud architects, cloud administrators, system engineers , and SysOps/DevOps engineers. Individuals using GCP to create, integrate, or modernize solutions using secure, scalable microservices architectures in hybrid environments.
	Pre Requisites: To get the most out of this course, students should have completed the Architecting with Google Kubernetes Engine course and its prerequisites, or have equivalent experience.
	Duration: 2
	Topics: Anthos Overview Describe challenges of hybrid cloud Discuss modern solutions Describe the Anthos Technology Stack Managing Hybrid Clusters using Kubernetes Engine Understand Anthos GKE hybrid environments, with Admin and User clusters Register and authenticate remote Anthos GKE clusters in GKE Hub View and manage registered clusters, in cloud and on-premises, using GKE Hub View workloads in all clusters from GKE Hub Lab: Managing Hybrid Clusters using Kubernetes Engine Introduction to Service Mesh Understand service mesh, and problems it solves Understand Istio architecture and components Explain Istio on GKE add on and it’s lifecycle, vs OSS Istio Understand request network traffic flow in a service mesh Create a GKE cluster, with a service mesh Configure a multi-service application with service mesh Enable external access using an ingress gateway Explain the multi-service example applications: Hipster Shop, and Bookinfo Lab: Installing Open Source Istio on Kubernetes Engine Lab: Installing the Istio on GKE Add-On with Kubernetes Engine Observing Services using Service Mesh Adapters Understand service mesh flexible adapter model Understand service mesh telemetry processing Explain Stackdriver configurations for logging and monitoring Compare telemetry defaults for cloud and on-premises environments Configure and view custom metrics using service mesh View cluster and service metrics with pre-configured dashboards Trace microservice calls with timing data using service mesh adapters Visualize and discover service attributes with service mesh Lab: Telemetry and Observability with Istio Managing Traffic Routing with Service Mesh Understand the service mesh abstract model for traffic management Understand service mesh service discovery and load balancing Review and compare traffic management use cases and configurations Understand ingress configuration using service mesh Visualize traffic routing with live generated requests Configure a service mesh gateway to allow access to services from outside the mesh Apply virtual services and destination rules for version-specific routing Route traffic based on application-layer configuration Shift traffic from one service version to another, with fine-grained control, like a canary deployment Lab: Managing Traffic Routing with Istio and Envoy Managing Policies and Security with Service Mesh Understand authentication and authorization in service mesh Explain mTLS flow for service to service communication Adopt mutual TLS authentication across the service mesh incrementally Enable end-user authentication for the frontend service Use service mesh access control policies to secure access to the frontend service Lab: Managing Policies and Security with Service Mesh Managing Policies using Anthos Config Management Understand the challenge of managing resources across multiple clusters Understand how a Git repository is as a configuration source of truth Explain the Anthos Config Management components, and object lifecycle Install and configure Anthos Config Management, operators, tools, and related Git repository Verify cluster configuration compliance and drift management Update workload configuration using repo changes Lab: Managing Policies in Kubernetes Engine using Anthos Config Configuring Anthos GKE for Multi-Cluster Operation Understand how multiple clusters work together using DNS, root CA, and service discovery Explain service mesh control-plane architectures for multi-cluster Configure a multi-service application using service mesh across multiple clusters with multiple control-planes Configure a multi-service application using service mesh across multiple clusters with a shared control-plane Configure service naming/discovery between clusters Review ServiceEntries for cross-cluster service discovery Migrate workload from a remote cluster to an Anthos GKE cluster Lab: Configuring GKE for Multi-Cluster Operation with Istio Lab: Configuring GKE for Shared Control Plane Multi-Cluster Operation

13.	Company: Google	Category: Google Cloud		Sub Category: Data & Machine Learning
	Course No.: GCP-320-IN		Course Name: From Data to Insights with Google Cloud Platform
	Description:&nbsp: Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This From Data to Insights with Google Cloud Platform specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL.
	Objectives: Upon completion of the From Data to Insights with Google Cloud Platform course, students will be able to: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance
	Audience: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform
	Pre Requisites: Basic proficiency with ANSI SQL (reference)
	Duration: 3
	Topics: Introduction to Data on the Google Cloud PlatformHighlight Analytics Challenges Faced by Data AnalystsIntro to Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Module 2: Analyzing Large Datasets with BigQuery Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: BigQuery Basics Exploring your Public Dataset with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Explore your Ecommerce Dataset with SQL in Google BigQuery Cleaning and Transforming your Data with Cloud Dataprep Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Creating a Data Transformation Pipeline with Cloud Dataprep Visualizing Insights and Creating Scheduled Queries Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery Storing and Ingesting new Datasets Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Ingesting New Datasets into BigQuery Enriching your Data Warehouse with JOINs Merge Historical Data Tables with UNION I ntroduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Troubleshooting and Solving Data Join Pitfalls Partitioning your Queries and Tables for Advanced Insights Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Creating Date-Partitioned Tables in BigQuery Designing Schemas that Scale: Arrays and Structs in BigQuery Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data Lab: Schema Design for Performance: Arrays and Structs in BigQuery Optimizing Queries for Performance Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Controlling Access with Data Security Best Practices Data Security Best Practices Controlling Access with Authorized Views Predicting Visitor Return Purchases with BigQuery ML Intro to ML Feature Selection Model Types Machine Learning in BigQuery Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML Deriving Insights from Unstructured Data using Machine Learning Structured vs Unstructured ML Prebuilt ML models Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs Lab: Training with Pre-built ML Models using Cloud Vision API and AutoML Completion Summary and course wrap-up

14.	Company: Google	Category: Google Cloud		Sub Category: Data & Machine Learning
	Course No.: GCP-330-IN		Course Name: Preparing for the Professional Data Engineer Examination
	Description:&nbsp: This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. Students can use this course to help create their own custom preparation plan. It helps distinguish what they know from what they don’t know. And it helps students develop and practice skills required of practitioners who perform this job. The course follows the organization of the Exam Guide outline, presenting highest-level concepts, ‘touchstones’, for students to determine whether they feel confident about their knowledge of that area and its dependent concepts, or if they want more study. They also will learn about and have the opportunity to practice key job skills, including cognitive skills such as case analysis, identifying technical watchpoints, and developing proposed solutions. These are job skills that are also exam skills. They will also test their basic abilities with Activity Tracking Challenge Labs. And they will have many sample questions similar to those on the exam, including solutions. The end of the course contains an ungraded practice exam quiz, followed by a graded practice exam quiz that simulates the exam-taking experience.
	Objectives: Upon completion of the Preparing for the Professional Data Engineer Examination course, students will be able to: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning
	Audience: Cloud professionals who intend to take the Professional Data Engineer certification exam Knowledge and experience with GCP, equivalent to GCP Big Data and Machine Learning Fundamentals course Knowledge of data engineering and ML solutions, equivalent to Data Engineering on GCP course Industry experience with data engineering and cloud computing .
	Pre Requisites: Familiarity with Google Cloud Platform to the level of the Data Engineering on Google Cloud Platform course (suggested, not required)
	Duration: 1
	Topics: Understanding the Professional Data Engineer Certification Establish basic knowledge about the certification exam and eliminate any confusion or misunderstandings about the process and nature of the exam itself. Sample Case Studies for the Professional Data Engineer Exam In-depth review of the Case Studies provided for exam preparation Designing and Building (Review and preparation tips) Tips and examples covering data processing systems design skills, data structures, and database skills that could be tested on the exam. Analyzing and Modeling (Review and preparation tips) Tips and examples covering data analysis, analysis and optimization of business processes, and machine learning skills that could be tested on the exam. Reliability, Policy, and Security (Review and preparation tips) Tips and examples covering reliability, policies, security, and compliance skills that could be tested on the exam. Resources and next steps Resources for learning more about identified subjects that could be tested on the exam.

15.	Company: Google	Category: Google Cloud		Sub Category: Data & Machine Learning
	Course No.: GCP-335-IN		Course Name: Machine Learning on Google Cloud Platform
	Description:&nbsp: What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
	Objectives: Upon completion of the Machine Learning with TensorFlow on Google Cloud Platform course, students will be able to: Think strategically and analytically about ML as a business process and consider the fairness implications with respect to ML How ML optimization works and how various hyperparameters affect models during optimization How to write models in TensorFlow using both pre-made estimators as well as custom ones and train them locally or in Cloud AI Platform Why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep
	Audience: Data Engineers and programmers interested in learning how to apply machine learning in practice Anyone interested in learning how to leverage machine learning in their enterprise
	Pre Requisites: Experience coding in Python Knowledge of basic statistics Knowledge of SQL and cloud computing (helpful)
	Duration: 5
	Topics: How Google Does Machine LearningWhat is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important not to skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them. Develop a data strategy around machine learning Examine use cases that are then reimagined through an ML lens Recognize biases that ML can amplify Leverage Google Cloud Platform tools and environment to do ML Learn from Google’s experience to avoid common pitfalls Carry out data science tasks in online collaborative notebooks Invoke pre-trained ML models from Cloud Datalab Launching into Machine LearningStarting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Identify why deep learning is currently popular Optimize and evaluate models using loss functions and performance metrics Mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets Intro to TensorFlowWe introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance. predictions using Cloud Machine Learning Engine Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf_estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine Feature EngineeringA key component of building effective machine learning models is to convert raw data to features in a way that allows ML to learn important characteristics from the data. We discuss how to represent features and code this up in TensorFlow. Human insight can be brought to bear in machine learning problems through the use of custom feature transformations. In this module, we talk about common types of transformations and how to implement them at scale. Turn raw data into feature vectors Preprocess and create new feature pipelines with Cloud Dataflow Create and implement feature crosses and assess their impact Write TensorFlow Transform code for feature engineering The Art and Science of MLMachine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of ML problems. We discuss regularization, dealing with sparsity, multi-class neural networks, reusable embeddings, and many other essential concepts and principles. Optimize model performance with hyperparameter tuning Experiment with neural networks and fine-tune performance Enhance ML model features with embedding layers Create reusable custom model code with the Custom Estimator

16.	Company: Google	Category: Google Cloud		Sub Category: Your Gateway to Google Cloud Platform
	Course No.: GCP-420-IN		Course Name: Google Cloud Platform Fundamentals for AWS Professionals
	Description:&nbsp: This course teaches AWS professionals about the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for AWS system administrators, solutions architects, and SysOps administrators who are familiar with AWS features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.
	Objectives: Upon completion of the Google Cloud Platform Fundamentals for AWS Professionals course, students will be able to: Identify Google Cloud counterparts for AWS IaaS, AWS PaaS, AWS SQL, AWS Blob Storage, AWS Application Insights, and AWS Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences
	Audience: Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs.
	Pre Requisites: Have basic proficiency with networking technologies like subnets and routing Have basic proficiency with command-line tools Students are expected to have experience with Amazon VPC, Amazon EC2 instances, and disks. Familiarity with Amazon S3 and AWS database technologies is recommended.
	Duration: 1
	Topics: Module 1: Introducing Google Cloud Platform Explain the advantages of Google Cloud. Define the components of Google’s network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Module 2: Getting Started with Google Cloud Identify the purpose of projects on Google Cloud Platform. Understand how AWS’s resource hierarchy differs from Google Cloud’s. Understand the purpose of and use cases for Identity and Access Management. Understand how AWS IAM differs from Google Cloud IAM. List the methods of interacting with Google Cloud Platform. Launch a solution using Cloud Marketplace. Module 3: Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Understand how Amazon VPC differs from Google VPC. Understand the similarities and differences between Amazon EC2 and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in AWS. Deploy applications using Google Compute Engine. Module 4: Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Amazon S3 and Amazon Glacier compare to Cloud Storage. Compare Google Cloud’s managed database services with Amazon RDS and Amazon Aurora. Learn how to choose among the various storage options on Google Cloud Platform. Load data from Cloud Storage into BigQuery. Perform a query on the data in BigQuery. Module 5: Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) differ from GKE. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl. Module 6: Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Amazon Elastic Beanstalk. Understand the purpose of and use cases for Google Cloud Endpoints. Module 7: Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Cloud Deployment Manager differs from AWS CloudFormation. Understand the purpose of integrated monitoring, alerting, and debugging. Understand how Google Monitoring differs from Amazon CloudWatch and AWS CloudTrail. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from AWS Data Lake. Understand how Google Cloud Pub/Sub differs from AWS Event Hubs and Service Bus. Understand how Google Cloud’s machine-learning APIs differ from AWS’s. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data. Summary and Review Review the products that make up Google Cloud and remember how to choose among them. Understand next steps for training and certification. Understand, at a high level, the process of migrating from AWS to Google Cloud.

17.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-425-IN		Course Name: Google Cloud Fundamentals for Azure Professionals
	Description:&nbsp: This course teaches Azure professionals about the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, solutions architects, and SysOps administrators who are familiar with Azure features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.
	Objectives: Upon completion of the Google Cloud Fundamentals for Azure Professionals course, students will be able to: Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences
	Audience: Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs.
	Pre Requisites: Have basic proficiency with networking technologies like subnets and routing Have basic proficiency with command-line tools Have experience with Microsoft Azure and IIS
	Duration: 1
	Topics: Module 1 Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google’s network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a- Service (IaaS) and Platform-as-a-Service (PaaS). Module 2 Getting Started with Google Cloud Identify the purpose of projects on Google Cloud. Understand how Azure’s resource hierarchy differs from Google Cloud’s. Understand the purpose of and use cases for Identity and Access Management. Understand how Azure AD differs from Google Cloud IAM. List the methods of interacting with Google Cloud. Launch a solution using Cloud Marketplace. Module 3 Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud. Understand how Azure VPC differs from Google VPC. Understand the similarities and differences between Azure VM and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure. Deploy applications using Google Compute Engine. Module 4 Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Azure Blob compares to Cloud Storage. Compare Google Cloud’s managed database services with Azure SQL. Learn how to choose among the various storage options on Google Cloud. Load data from Cloud Storage into BigQuery. Module 5 Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl. Module 6 Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Azure App Service. Understand the purpose of and use cases for Google Cloud Endpoints. Module 7 Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Google Cloud Deployment Manager differs from Azure Resource Manager. Understand the purpose of integrated monitoring, alerting, and debugging Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Module 8 Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from Azure Data Lake. Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus. Understand how Google Cloud’s machine-learning APIs differ from Azure’s. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data. Module 9 Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from Azure to Google Cloud.

18.	Company: Google	Category: Google Cloud		Sub Category: Your Gateway to Google Cloud Platform
	Course No.: GCP-430-IN		Course Name: Getting Started with Google Kubernetes Engine
	Description:&nbsp: This one-day instructor-led Getting Started with Google Kubernetes Engine class equips students to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates.
	Objectives: Upon completion of the Getting Started with Google Kubernetes Engine course, students will be able to: Understand container basics. Containerize an existing application. Understand Kubernetes concepts and principles. Deploy applications to Kubernetes using the CLI. Set up a continuous delivery pipeline using Jenkins
	Audience: Application developers, Cloud Solutions Architects, DevOps Engineers, IT managers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.
	Pre Requisites: Basic proficiency with command-line tools and Linux operating system environments, as well as Web server Systems Operations experience including deploying and managing applications, either on-premises or in a public cloud environment.
	Duration: 1
	Topics: Introduction to Containers and Docker Create a container. Package a container using Docker. Store a container image in Google Container Registry. Launch a Docker container. Kubernetes Basics Provision a complete Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl. Break an application into microservices using Kubernetes’ Deployments and Services. Deploying to Kubernetes Create a Kubernetes deployment. Trigger, pause, resume, and rollback updates. Understand and build canary deployments. Creating a Continuous Delivery Pipeline Provision Spinnaker or Jenkins in your Kubernetes cluster. Manage application code in a source repository that can trigger code changes to a continuous delivery pipeline. Create a continuous delivery pipeline and start it manually or automatically with a code change. Implement a canary deployment that hosts two versions of your application in production for release testing

19.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-435-IN		Course Name: Architecting with Google Kubernetes Engine
	Description:&nbsp: This Architecting with Google Kubernetes Engine course introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, students explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring.
	Objectives: Upon completion of the Architecting with Google Kubernetes Enginecourse, students will be able to: Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Platform Understand how pod networking works in Kubernetes Engine Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/ kubectl commands Launch, roll back and expose jobs in Kubernetes Manage access control using Kubernetes RBAC and Google Cloud IAM Managing pod security policies and network policies Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts Understand GCP choices for managed storage services Monitor applications running in Kubernetes Engine
	Audience: This course is intended for the following participants: Cloud architects, administrators, and SysOps/DevOps personnel Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.
	Pre Requisites: To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience Basic proficiency with command-line tools and Linux operating system environments
	Duration: 3
	Topics: Introduction to Google Cloud Platform Use the Google Cloud Platform Console Use Cloud Shell Define cloud computing Identify GCPs compute services Understand regions and zones Understand the cloud resource hierarchy Administer your GCP resources Containers and Kubernetes in GCP Create a container using Cloud Build Store a container in Container Registry Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE) Understand how to choose among GCP compute platforms Kubernetes Architecture Understand the architecture of Kubernetes: pods, namespaces Understand the control-plane components of Kubernetes Create container images using Google Cloud Build Store container images in Google Container Registry Create a Kubernetes Engine cluster Kubernetes Operations Work with the kubectl command Inspect the cluster and Pods View a Pods console output Sign in to a Pod interactively Deployments, Jobs, and Scaling Create and use Deployments Create and run Jobs and CronJobs Scale clusters manually and automatically Configure Node and Pod affinity Get software into your cluster with Helm charts and Kubernetes Marketplace GKE Networking Create Services to expose applications that are running within Pods Use load balancers to expose Services to external clients Create Ingress resources for HTTP(S) load balancing Leverage container-native load balancing to improve Pod load balancing Define Kubernetes network policies to allow and block traffic to pods Persistent Data and Storage Use Secrets to isolate security credentials Use ConfigMaps to isolate configuration artifacts Push out and roll back updates to Secrets and ConfigMaps Configure Persistent Storage Volumes for Kubernetes Pods Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts Access Control and Security in Kubernetes and Kubernetes Engine Understand Kubernetes authentication and authorization Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources Define Kubernetes pod security policies Understand the structure of GCP IAM Define IAM roles and policies for Kubernetes Engine cluster administration Logging and Monitoring Use Stackdriver to monitor and manage availability and performance Locate and inspect Kubernetes logs Create probes for wellness checks on live applications Using GCP Managed Storage Services from Kubernetes Applications Understand pros and cons for using a managed storage service versus self-managed containerized storage Enable applications running in GKE to access GCP storage services Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application

20.	Company: Google	Category: Google Cloud		Sub Category: Data & Machine Learning
	Course No.: GCP-440-IN		Course Name: Google Cloud Platform Big Data and Machine Learning Fundamentals
	Description:&nbsp: This Google Cloud Platform Big Data and Machine Learning Fundamentals course introduces students to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
	Objectives:
	Audience:
	Pre Requisites: Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: A common query language such as SQL Extract, transform, load activities Data modeling Machine learning and/or statistics Programming in Python
	Duration: 1
	Topics: Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources

21.	Company: Google	Category: Google Cloud		Sub Category: Your Gateway to Google Cloud Platform
	Course No.: GCP-455-IN		Course Name: Machine Learning for Business Professionals
	Description:&nbsp: If you’re wondering what the machine learning hype is about or want to know what it can do for your enterprise, without the technical jargon, this course is for you. In this Machine Learning for Business Professionals course students will learn what machine learning is, how to translate business problems into machine learning use cases, how to vet those use cases for feasibility and impact, how to discover unexpected use cases, how to carry a machine learning project through its various phases, and how to pursue machine learning and artificial intelligence responsibly and ethically.
	Objectives: Upon completion of the Machine Learning for Business Professionalscourse, students will be able to: Formulate machine learning solutions to real-world problems Identify whether the data you have is sufficient for ML Carry a project through various ML phases including training, evaluation, and deployment Perform AI responsibly and avoid reinforcing existing bias Discover ML use cases
	Audience: Traditional enterprise business decision makers
	Pre Requisites: You’ll need a desktop web browser to run this course’s interactive labs via Qwiklabs and Google Cloud Platform.
	Duration: 1
	Topics: IntroductionThis module reviews the learning objectives for the course and introduces technology that will be important for completing labs.What is Machine Learning?This module defines what machine learning is, provides examples of how businesses are using it, contextualizes recent advances in machine learning, and reviews how artificial intelligence raises important ethical questions.Employing MLThis module reviews how to do machine learning, including how to label data, train and evaluate models and avoid reinforcing bias.Discovering ML Use CasesThis module reviews broad categories of ML use cases in order to jump start your ideation.How to be successful at MLThis module reviews what your business must do in order to be successful at ML, including how to acquire data, how to appropriately govern that data, and how to create a culture of innovation.SummaryThis module reviews the content in the course.

22.	Company: Google	Category: Google Cloud		Sub Category: Cloud Platform Architect
	Course No.: GCP-460-IN		Course Name: Architecting with Google Compute Engine
	Description:&nbsp: This Architecting with Google Compute Engine course introduces students to the comprehensive and flexible infrastructure and platform services provided by Google Cloud, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, students explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
	Objectives: Upon completion of the Architecting with Google Compute Enginecourse, students will be able to: Consider the entire range of Google Cloud Platform technologies in their plans Learn methods to develop, implement, and deploy solutions Distinguish between features of similar or related products and technologies Recognize a wide variety of solution domains, use cases, and applications Develop essential skills for managing and administering solutions Develop knowledge of solution patterns, methods, technologies, and designs that are used to implement security, scalability, high availability, and other desired qualities
	Audience: Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with a focus on Google Compute Engine.
	Pre Requisites: Completion of Google Cloud Platform Fundamentals or equivalent experience, Basic proficiency with command-line tools and Linux operating system environments Systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment.
	Duration: 3
	Topics: Introduction to Google Cloud List the different ways of interacting with Google Cloud. Use the Cloud Console and Cloud Shell. Create Cloud Storage buckets. Use the Google Cloud Marketplace to deploy solutions. Virtual Networks List the VPC objects in Google Cloud. Differentiate between the different types of VPC networks. Implement VPC networks and firewall rules. Implement Private Google Access and Cloud NAT. Virtual Machines Recall the CPU and memory options for virtual machines. Describe the disk options for virtual machines. Explain VM pricing and discounts. Use Compute Engine to create and customize VM instances. CloudIAM Describe the Cloud IAM resource hierarchy. Explain the different types of IAM roles. Recall the different types of IAM members. Implement access control for resources using Cloud IAM. Storage and Database Services Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable. Choose a data storage service based on your requirements. Implement data storage services. Resource Management Describe the cloud resource manager hierarchy. Recognize how quotas protect Google Cloud customers. Use labels to organize resources. Explain the behavior of budget alerts in Google Cloud. Examine billing data with BigQuery. Resource Monitoring Describe the services for monitoring, logging, error reporting, tracing, and debugging. Create charts, alerts, and uptime checks for resources with Cloud Monitoring. Use Cloud Debugger to identify and fix errors. Interconnecting Networks Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud. Determine which Google Cloud interconnect or peering service to use in specific circumstances. Create and configure VPN gateways. Recall when to use Shared VPC and when to use VPC Network Peering. Load Balancing and Autoscaling Recall the various load balancing services. Determine which Google Cloud load balancer to use in specific circumstances. Describe autoscaling behavior. Configure load balancers and autoscaling. Infrastructure Modernization Automate the deployment of Google Cloud services using Deployment Manager or Terraform. Outline the Google Cloud Marketplace. Managed Services Describe the managed services for data processing in Google Cloud.

23.	Company: Google	Category: Google Cloud		Sub Category: Your Gateway to Google Cloud Platform
	Course No.: GCP-465-IN		Course Name: Leading Change in the Cloud Era
	Description:&nbsp: This Leading Change in the Cloud Era course will help managers to identify ways to increase their adaptability as a leader, identify actions to effectively manage transition and coach others through change, and increase their ability to communicate change to gain commitment.
	Objectives:
	Audience: People managers, leaders and executive sponsors in the process of moving to Google Cloud Platform (GCP) This is a private class intended for a Single Customer, to bring together Key Stakeholders & Project team to work towards a common goal.
	Pre Requisites:
	Duration: 1
	Topics: Module 1: Change in the Cloud Era Cloud Change Journey Customer Story Google Cloud Adoption Framework Module 2: How People React to Change Understand the psychology of change. Evaluate your self-awareness about the impact of change on your team. Assess where your team is on the change curve and how you can support them. Module 3: Leading change in Google Cloud Platform Recognize the most common effects moving to the cloud can have on your team. Create a communication plan to lead others and deal with resistance. Module 4: PSO Consulting Services & Next steps Recognize the PSO Consulting Services available to support your Google Cloud journey.

24.	Company: Google	Category: Google Cloud		Sub Category: Application Development
	Course No.: GCP-470-IN		Course Name: Logging, Monitoring and Observability in Google Cloud
	Description:&nbsp: This three-day instructor-led course teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
	Objectives: This course teaches participants the following skills: Plan and implement a well-architected logging and monitoring infrastructure Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) Create effective monitoring dashboards and alerts Monitor, troubleshoot, and improve Google Cloud infrastructure Analyze and export Google Cloud audit logs Find production code defects, identify bottlenecks, and improve performance Optimize monitoring costs
	Audience: This class is intended for the following participants: Cloud architects, administrators, and SysOps personnel Cloud developers and DevOps personnel
	Pre Requisites: To get the most out of this course, participants should have: Completion of Google Cloud Platform Fundamentals: Core Infrastructure or equivalent experience Basic scripting or coding familiarity Proficiency with command-line tools and Linux operating system environments
	Duration: 3
	Topics: Module 1: Introduction to Google Cloud Monitoring Tools Understand the purpose and capabilities of Google Cloud components: Logging, Monitoring, Error Reporting, and Service Monitoring Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace, and Profiler Module 2: Avoiding Customer Pain Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation Measure customer pain with SLIs Define critical performance measures Create and use SLOs and SLAs Achieve developer and operation harmony with error budgets Module 3: Alerting Policies Develop alerting strategies Define alerting policies Add notification channels Identify types of alerts and common uses for each Construct and alert on resource groups Manage alerting policies programmatically Module 4: Monitoring Critical Systems Choose best practice monitoring project architectures Differentiate Cloud IAM roles for monitoring Use the default dashboards appropriately Build custom dashboards to show resource consumption and application load Define uptime checks to track aliveness and latency Module 5: Configuring Google Cloud Services for Observability Integrate logging and monitoring agents into Compute Engine VMs and images Enable and utilize Kubernetes Monitoring Extend and clarify Kubernetes monitoring with Prometheus Expose custom metrics through code, and with the help of OpenCensus Module 6: Advanced Logging and Anaylsis Identify and choose among resource tagging approaches Define log sinks (inclusion filters) and exclusion filters Create metrics based on logs Define custom metrics Link application errors to Logging using Error Reporting Export logs to BigQuery Module 7: Monitoring Network Security and Audit Logs Collect and analyze VPC Flow logs and Firewall Rules logs Enable and monitor Packet Mirroring Explain the capabilities of Network Intelligence Center Use Admin Activity audit logs to track changes to the configuration or metadata of resources Use Data Access audit logs to track accesses or changes to user-provided resource data Use System Event audit logs to track GCP administrative actions Module 8: Managing Incidents Define incident management roles and communication channels Mitigate incident impact Troubleshoot root causes Resolve incidents Document incidents in a post-mortem process Module 9: Investigating Application Performance Issues Debug production code to correct code defects Trace latency through layers of service interaction to eliminate performance bottlenecks Profile and identify resource-intensive functions in an application Module 10: the Costs of Monitoring Analyze resource utilization cost for monitoring related components within Google Cloud Implement best practices for controlling the cost of monitoring within Google Cloud

25.	Company: Google	Category: Google-Application Development		Sub Category: google-Application Development-Application Development
	Course No.: GCP-475-IN		Course Name: Managing Google Cloud’s Apigee API Platform for Hybrid Cloud
	Description:&nbsp: This Managing Google Cloud’s Apigee API Platform for Hybrid Cloud course introduces students to fundamentals and advanced practices applicable to the installation and management of Google Cloud’s Apigee API Platform for hybrid cloud. Through a combination of lectures, hands-on labs, and supplemental materials, students will learn how to design, install, secure, manage, and scale Apigee API Platform.
	Objectives: This Managing Google Cloud’s Apigee API Platform for Hybrid Cloud course teaches participants the following skills: Identify the purpose and value of Apigee API Platform. Gain detailed understanding of Apigee API Platform architecture and recommended practices for topology design. Gain detailed understanding of Apigee terminology and logical organizational structures. Interact with Apigee API Platform and solve scenarios by leveraging recommended practices. Understand capabilities and practices to secure, manage and scale the platform. Understand and put in practice installation and upgrade processes.
	Audience: Cloud Architects, Cloud Engineers and Operation Specialists
	Pre Requisites: Familiarity with Linux command and command line interface Basic understanding of Google Cloud. Basic understanding of Anthos and GKE. Basic understanding of networking. Basic understanding of shell script, XML, JSON, HTTP, REST, and TLS.
	Duration: 3
	Topics: Module 1: Fundamentals Familiarize with Apigee product capabilities and its place in the enterprise architecture. Familiarize with Google Cloud, Kubernetes and Anthos concepts. Discuss REST fundamentals. Module 2: Architecture Discuss Apigee hybrid architecture. Gain understanding of Apigee technology stack. Discuss Apigee terminology and organizational structure. Familiarize with Apigee API. Module 3: Installation and Platform Operation Discuss installation process. Discuss installation prerequisites. Gain detail understanding of and practice installation process. Discuss post installation tasks and recurrent activities recommended for the platform. Gain understanding common operational capabilities and practices. Discuss backup and restore strategy. Module 4: Deployment and Environment Management Familiarize with API proxy concept and deployment process. Familiarize with API tracing and debugging. Discuss Developer Portal solutions. Gain understanding of and practice capabilities available for environment management. Module 5: Security Discuss infrastructure security. Discuss data storage and encryption. Discuss hybrid access management capabilities and RBAC. Module 6: Capacity Planning and Scaling Discuss capacity planning considerations. Gain understanding of and practice capabilities to scale up/down runtime components. Module 7: Upgrade Familiarize with upgrade process. Discuss rollback process. Module 8: Logging and Monitoring Familiarize with logging capabilities. Understand metrics generated and captured by the system. Discuss Apigee analytics. Discuss monitoring and troubleshooting techniques. Familiarize with Apigee support process.

26.	Company: Google	Category: Google-Application Development		Sub Category: google-Application Development-Application Development
	Course No.: GCP-480-IN		Course Name: Developing APIs with Google Cloud’s Apigee API Platform
	Description:&nbsp: Learn the fundamentals of API Design and the out-of-the-box capabilities offered by Google Cloud’s Apigee API Platform. This course features a combination of lectures, hands-on labs, and supplemental materials to show you how to design, build, secure, deploy, and manage API solutions.
	Objectives: The Developing APIs with Google Cloud’s Apigee API Platform course teaches participants the following skills: Identify the purpose and value of Google Cloud’s Apigee API Platform. Develop a good understanding of Google Cloud’s Apigee API Platform terminology and organizational model. Interact with Google Cloud’s Apigee API Platform. Solve scenarios by leveraging APIs, recommended practices, and an API-first strategy. Understand and put in practice the API lifecycle. Identify capabilities available to secure, scale, and manage APIs and API products.
	Audience: Developers, architects, or engineers responsible for the solutioning, design, implementation, or management of APIs, API products, or digital products that leverage APIs.
	Pre Requisites: Familiarity with HTTP, XML, Javascript.
	Duration: 3
	Topics: Module 1: Apigee Overview Understand the placement and role of API management in modern application development. Define Apigee API Platform logical components and organizational structure. Differentiate between Apigee flexible deployment models. Explain API lifecycle. Module 2: API First and OpenAPI Specifications Describe REST API design. Understand the value of API-First development and how to apply it. Discuss OpenAPI specification and its use in the context of API-First development. API Proxies Define the building blocks of APIs and API proxies. Describe how APIs proxies work and how capabilities such as flows, policies, route rules, virtual hosts and target servers play a role. Understand how APIs are exposed. Understand how API proxies connect to backend systems. Module 4: API Products Define API products and the value behind API product strategy. Understand the role of developers, applications, and API keys in API management. Describe the API publication process. Understand API responses and status codes for REST APIs. Module 5: Authentication, Authorization, and OAuth Discuss the importance of API Security. Understand the value of application identity. Understand the role of user authentication and authorization. Gain deep understanding of OAuth (Access token, Refresh token, Common pattern for all grant types) and its application in the context of API design and management. Discuss federated identity and the use of JSON Web Tokens in your API proxies. Module 6: Content, Transport, and Internal Security Explore platform capabilities for protecting against content-based attacks and transport security. Understand how to protect sensitive data using encrypted KVMs, data masking, private variables. Module 7: Mediation Understand the out-of-the-box platform capabilities for implementing mediation and fault handling. Describe implementation patterns and policies for JSON, XML, and SOAP. Understand extensibility options using Service Callouts, JavaScript, Python, and hosted targets. Explore development practices and capabilities used to reuse, share, and enforce execution of flows and policies. Illustrate out-of-the-box options to invoke Google Cloud services and third-party components using extensions. Module 8: Traffic Management Describe when and how to use traffic management. Evaluate options and applicable use cases for rate limiting with spike arrests and quotas. Understand caching strategy and how to apply it. Module 9: API Publishing Describe API publishing strategy and process. Expand understanding of REST API design by discussing API versioning. Describe the role of developer portals in the API lifecycle and as a critical part of API strategy. Module 10: Logging and Analytics Discuss available options for message logging. Understand the value and use of API analytics. Differentiate between message logging and API analytics. Understand the extensibility options available for API analytics using custom metrics and dimensions. Module 11: Advanced Topics Discuss recommended practices and tooling for Apigee offline development. Describe the capabilities offered by management API. Evaluate options to leverage CI/CD as part of API lifecycle. Explore Apigee Deployment Options.

27.	Company: Google	Category: Google-Data and Machine Learning		Sub Category: google-Data and Machine Learning-Data Engineering
	Course No.: GCP-500-IN		Course Name: Customer Experiences with Contact Center AI
	Description:&nbsp: During this 4 day course, students will learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). They will also learn some best practices for integrating conversational solutions with their existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale.
	Objectives: After completing the Customer Experiences with Contact Center AI course, students will be able to: Define what Google Contact Center AI is. Explain how Dialogflow can be used in contact center applications. Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. Implement a chat virtual agent. Implement a voice virtual agent. Describe options for storing parameters and fulfilling user requests. Deploy a virtual agent to production. Identify best practices for design and deployment of virtual agents. Identify key aspects, such as security and compliance in the context of contact centers.
	Audience: Architects and systems integrators implementing Contact Center AI Conversational Architects Contact center virtual agent and application developers Business managers
	Pre Requisites: Completed Google Cloud Product Fundamentals or have equivalent experience. Desirable but not required: Knowledge of a programming language such as Python or JavaScript.
	Duration: 4
	Topics: Overview of Contact Center AI Define what Contact Center AI (CCAI) is and what it can do for contact centers. Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights. Describe the role each component plays in a CCAI solution. Conversational Experiences List the basic principles of a conversational experience. Explain the role of conversation virtual agents in a conversation experience. Articulate how STT (speech to text) can determine the quality of a conversation experience. Demonstrate and test how speech adaptation can improve the speech recognition accuracy of the agent. Recognize the different NLU (natural language understanding) and NLP (natural language processing) techniques and the role they play in conversati experiences. Explain the different elements of a conversation (intents, entities, etc.). Use sentiment analysis to help with the achievement of a higher-quality conversation experience. Improve conversation experiences by choosing different TTS voices (Wavenet vs. Standard). Modify the speed and pitch of a synthesized voice. Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage. Fundamentals of Building Conversations with Dialogflow Identify user roles and their journeys. Write personas for virtual agents and users. Model user-agent interactions. List the basic elements of the Dialogflow user interface. Build a virtual agent to handle identified user journeys. Train the NLU model through the Dialogflow console. Define and test intents for a basic agent. Train the agent to handle expected and unexpected user scenarios. Recognize the different types of entities and when to use them. Create entities. Define and test entities on a basic agent. Implement slot filling using the Dialogflow UI. Describe when Mega Agent might be used. Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ Maintaining Context in a Conversation Create follow-up intents. Recognize the scenarios in which context should be used. Identify the possible statuses of a context (active versus inactive context). Implement dialogs using input and output contexts. Moving from Chat agent to Voice agent Describe two ways that the media type changes the conversation Configure the telephony gateway for testing Test a basic voice agent Modify the voice of the agent Show how the different media types can have different responses Consider the modifications needed when moving to production Be aware of the telephony integration for voice in a production environment Taking Actions with Fulfillment Define the role of fulfillment with respect to Contact Center AI. Characterize what needs to be collected in order to fulfill a request. Identify existing backend systems on the customer infrastructure. Use Firestore to store mappings returned from functions. Appreciate that the interaction with customers’ data storage will vary based their data warehouses. Implement fulfillment using Cloud Functions. Implement fulfillment using Python on AppEngine. Describe the use of Apigee for application deployment. Testing and Logging Debug a virtual agent by testing intent accuracy. Debug fulfillment by testing the different functions and integrations with backend systems through API calls. Implement version control to achieve more scalable collaboration. Log conversations using Cloud Logging. Recognize ways that audits can be performed. Intelligent Assistance for Live Agents Recognize use cases where Agent Assist adds value. Identify, collect, and curate documents for knowledge base construction. Set up knowledge bases. Describe how FAQ Assist works. Describe how Document Assist works. Describe how the Agent Assist UI works. Describe how Dialogflow Assist works. Describe how Smart Reply works. Describe how real-time entity extraction works. Drawing Insights from Recordings Analyze audio recordings using the Speech Analytics Framework (SAF). Integrating a Virtual Agent with Third Parties Use the Dialogflow API to programmatically create and modify the virtual agent. Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN. Replace existing head intent detection on IVRs with Dialogflow intents. Describe virtual agent integration with Google Assistant. Describe virtual agent integration with messaging platforms. Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk). Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio). Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design. Incorporate IVR features in the virtual agent. Environment Management Create Draft and Published versions of your virtual agent. Create environments where your virtual agent will be published. Load a saved version of your virtual agent to Draft. Change which version is loaded to an environment. Methods of Compliance with Federal Regulations Describe two ways that security can be implemented on a Contact Center AI integration. Identify current compliance measures and scenarios where compliance is needed. Best Practices for Virtual Agents Convert pattern matching and decision trees to smart conversational design. Recognize situations that require escalation to a human agent. Support multiple platforms, devices, languages, and dialects. Use Diagflow’s built-in analytics to assess the health of the virtual agent. Perform agent validation through the Dialogflow UI. Monitor conversations and Agent Assist. Institute a DevOps and version control framework for agent development and maintenance. Consider enabling spell correction to increase the virtual agent’s accuracy.

28.	Company: Google	Category: Google-Your Gateway to Google Cloud Platform		Sub Category: google-Your Gateway to Google Cloud Platform-Business
	Course No.: GCP-505-IN		Course Name: Customer Experiences with Contact Center AI – Dialogflow ES
	Description:&nbsp: Welcome to ‘Customer Experiences with Contact Center AI’ with a focus on Dialogflow ES. In this course, learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, virtual agent development utilizes Dialogflow ES. Students will also learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale.
	Objectives: After completing the Customer Experiences with Contact Center AI – Dialogflow ES course, students will be able to: Define what Contact Center AI (CCAI) is and what it can do for contact centers. Explain how Dialogflow can be used in contact center applications. Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. Implement a chat virtual agent using Dialogflow ES. Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. Describe options for storing parameters and fulfilling user requests. Describe how to deploy virtual agents to production. Identify best practices for development of virtual agents in Dialogflow ES. Identify key aspects, such as security and compliance, in the context of contact centers.
	Audience: This is a beginner to intermediate course, intended for learners with the following types of roles: Conversational designers: Designs the user experience of a virtual assistant. Translates the brand’s business requirements into natural dialog flows. Citizen developers: Creates new business applications for consumption by others using high level development and runtime environments. Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.
	Pre Requisites: Completed GCP Fundamentals or have equivalent experience
	Duration: 4
	Topics: The course includes presentations, demonstrations, and hands-on labs.Module 1: Overview of Contact Center AI Define what Contact Center AI (CCAI) is and what it can do for contact centers. Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI. Describe the role each component plays in a CCAI solution. Module 2: Conversational Experiences List the basic principles of a conversational experience. Explain the role of Conversation virtual agents in a conversation experience. Articulate how STT (Speech to Text) can determine the quality of a conversation experience. Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent. Recognize the different NLU (Natural Language Understanding) and NLP (Natural Language Processing) techniques and the role they play on conversation experiences. Explain the different elements of a conversation (intents, entities, etc). Use sentiment analysis to help with the achievement of a higher-quality conversation experience. Improve conversation experiences by choosing different TTS voices (Wavenet vs Standard). Modify the speed and pitch of a synthesized voice. Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage. Module 3: Fundamentals of Designing Conversations Identify user roles and their journeys. Write personas for virtual agents and users. Model user-agent interactions. Module 4: Dialogflow Product Options Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX). Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX. Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX. List the basic elements of the Dialogflow user interface. Module 5: Course Review Review what was covered in the course as relates to the objectives. Module 6: Fundamentals of building conversations with Dialogflow ES List the basic elements of the Dialogflow CX User Interface. Build a virtual agent to handle identified user journeys. Train the NLU model through the Dialogflow console. Define and test intents for a basic agent. Train the agent to handle expected and unexpected user scenarios. Recognize the different types of entities and when to use them. Create entities. Define and test entities on a basic agent. Implement slot filling using the Dialogflow UI. Describe when Mega Agent might be used. Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ. Module 7: Maintaining Context in a Conversation Create follow-up intents. Recognize the scenarios in which context should be used. Identify the possible statuses of a context (active versus inactive context). Implement dialogs using input and output contexts. Module 8: Moving From Chat to Voice Virtual Agent Describe two ways that the media type changes the conversation. Configure the telephony gateway for testing. Test a basic voice agent. Modify the voice of the agent. Show how the different media types can have different responses. Consider the modifications needed when moving to production. Be aware of the telephony integration for voice in a production environment. Module 9: Course Review Review what was covered in the course as relates to the objectives. Module 10: Testing and Logging Use Dialogflow tools for troubleshooting. Use Google Cloud tools for debugging your virtual agent. Review logs generated by virtual agent activity. Recognize ways an audit can be performed. Module 11: Taking Actions with Fulfillment Characterize the role of fulfillment with respect to Contact Center AI. Implement a virtual agent using Dialogflow ES. Use Cloud Firestore to store customer data. Implement fulfillment using Cloud Functions to read and write Firestore data. Describe the use of Apigee for application deployment. Module 12: Integrating Virtual Agents Describe how to use the Dialogflow API to programmatically create and modify the virtual agent. Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN. Describe how to replace existing head intent detection on IVRs with Dialogflow intents. Describe virtual agent integration with Google Assistant. Describe virtual agent integration with messaging platforms. Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk). Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio). Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design. Describe how to incorporate IVR features in the virtual agent. Module 13: Course Review Review what was covered in the course as relates to the objectives. Module 14: Environment Management Create Draft and Published versions of your virtual agent. Create environments where your virtual agent will be published. Load a saved version of your virtual agent to Draft. Change which version is loaded to an environment. Module 15: Drawing Insights from Recordings with SAF Analyze audio recordings using the Speech Analytics Framework (SAF). Module 16: Intelligence Assistance for Live Agents Recognize use cases where Agent Assist adds value. Identify, collect and curate documents for knowledge base construction. Describe how to set up knowledge bases. Describe how FAQ Assist works. Describe how Document Assist works. Describe how the Agent Assist UI works. Describe how Dialogflow Assist works. Describe how Smart Reply works. Describe how Real-time entity extraction works. Module 17: Compliance and Security Describe two ways security can be implemented on a CCAI integration. Identify current compliance measures and scenarios where compliance is needed. Module 18: Best Practices Convert pattern matching and decision trees to smart conversational design. Recognize situations that require escalation to a human agent. Support multiple platforms, devices, languages, and dialects. Use Diagflow’s built-in analytics to assess the health of the virtual agent. Perform agent validation through the Dialogflow UI. Monitor conversations and Agent Assist. Institute a DevOps and version control framework for agent development and maintenance. Consider enabling spell correction to increase the virtual agent’s accuracy. Module 19: Implementation Methodology Identify the stages of the Google Enterprise Sales Process. Describe the Partner role in the Enterprise Sales Process. Detail the steps in a Contact Center AI project using Google’s ESP. Describe the key activities of the Implementation Phase in ESP. Locate and understand how to use Google’s support assets for Partners. Module 20: Course Review Review what was covered in the course as relates to the objectives.

29.	Company: Google	Category: Google-Your Gateway to Google Cloud Platform		Sub Category: google-Your Gateway to Google Cloud Platform-Business
	Course No.: GCP-510-IN		Course Name: Customer Experiences with Contact Center AI – Dialogflow CX
	Description:&nbsp: Welcome to ‘Customer Experiences with Contact Center AI’ with a focus on Dialogflow CX. In this course, learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, virtual agent development utilizes Dialogflow CX. Students will also learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale.
	Objectives: After completing the Customer Experiences with Contact Center AI – Dialogflow CX course, students will be able to: Define what Contact Center AI (CCAI) is and what it can do for contact centers. Explain how Dialogflow can be used in contact center applications. Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. Implement a chat virtual agent using Dialogflow CX. Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. Describe options for storing parameters and fulfilling user requests. Describe how to deploy virtual agents to production. Identify best practices for development of virtual agents in Dialogflow CX. Identify key aspects, such as security and compliance, in the context of contact centers.
	Audience: This is a beginner to intermediate course, intended for learners with the following types of roles: Conversational designers: Designs the user experience of a virtual assistant. Translates the brand’s business requirements into natural dialog flows. Citizen developers: Creates new business applications for consumption by others using high level development and runtime environments. Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.
	Pre Requisites: Completed GCP Fundamentals or have equivalent experience
	Duration: 4
	Topics: The course includes presentations, demonstrations, and hands-on labs.Module 1: Overview of Contact Center AI Define what Contact Center AI (CCAI) is and what it can do for contact centers. Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI. Describe the role each component plays in a CCAI solution. Module 2: Conversational Experiences List the basic principles of a conversational experience. Explain the role of Conversation virtual agents in a conversation experience. Articulate how STT (Speech to Text) can determine the quality of a conversation experience. Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent. Recognize the different NLU (Natural Language Understanding) and NLP (Natural Language Processing) techniques and the role they play on conversation experiences. Explain the different elements of a conversation (intents, entities, etc). Use sentiment analysis to help with the achievement of a higher-quality conversation experience. Improve conversation experiences by choosing different TTS voices (Wavenet vs Standard). Modify the speed and pitch of a synthesized voice. Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage. Module 3: Fundamentals of Designing Conversations Identify user roles and their journeys. Write personas for virtual agents and users. Model user-agent interactions. Module 4: Dialogflow Product Options Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX). Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX. Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX. List the basic elements of the Dialogflow user interface. Module 5: Course Review Review what was covered in the course as relates to the objectives. Module 6: Fundamentals of Building Conversations with Dialogflow CX List the basic elements of the Dialogflow CX User Interface. Create entities. Create intents and form fill entities in training phrases. Train the NLU model through the Dialogflow console. Build a basic virtual agent to handle identified user journeys. Module 7: Scaling with Standalone Flows Recognize the scenarios in which standalone flows can help scale your virtual agent. Implement a flow that uses other flows. Module 8: Using Route Groups for Reusable Routes Define the concept of route groups with respect to Dialogflow CX. Create a route group. Recognize the scenarios in which route groups should be used. Identify the possible scope of a route group. Implement a flow that uses a route group. Module 9: Course Review Review what was covered in the course as relates to the objectives. Module 10: Testing and Logging Use Dialogflow tools for troubleshooting. Use Google Cloud tools for debugging your virtual agent. Review logs generated by virtual agent activity. Recognize ways an audit can be performed. Module 11: Taking Actions with Fulfillment Characterize the role of fulfillment with respect to Contact Center AI. Implement a virtual agent using Dialogflow ES. Use Cloud Firestore to store customer data. Implement fulfillment using Cloud Functions to read and write Firestore data. Describe the use of Apigee for application deployment. Module 12: Integrating Virtual Agents Describe how to use the Dialogflow API to programmatically create and modify the virtual agent. Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN. Describe how to replace existing head intent detection on IVRs with Dialogflow intents. Describe virtual agent integration with Google Assistant. Describe virtual agent integration with messaging platforms. Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk). Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio). Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design. Describe how to incorporate IVR features in the virtual agent. Module 13: Course Review Review what was covered in the course as relates to the objectives. Module 14: Environment Management Create Draft and Published versions of your virtual agent. Create environments where your virtual agent will be published. Load a saved version of your virtual agent to Draft. Change which version is loaded to an environment. Module 15: Drawing Insights from Recordings with SAF Analyze audio recordings using the Speech Analytics Framework (SAF). Module 16: Intelligence Assistance for Live Agents Recognize use cases where Agent Assist adds value. Identify, collect and curate documents for knowledge base construction. Describe how to set up knowledge bases. Describe how FAQ Assist works. Describe how Document Assist works. Describe how the Agent Assist UI works. Describe how Dialogflow Assist works. Describe how Smart Reply works. Describe how Real-time entity extraction works. Module 17: Compliance and Security Describe two ways security can be implemented on a CCAI integration. Identify current compliance measures and scenarios where compliance is needed. Module 18: Best Practices Convert pattern matching and decision trees to smart conversational design. Recognize situations that require escalation to a human agent. Support multiple platforms, devices, languages, and dialects. Use Diagflow’s built-in analytics to assess the health of the virtual agent. Perform agent validation through the Dialogflow UI. Monitor conversations and Agent Assist. Institute a DevOps and version control framework for agent development and maintenance. Consider enabling spell correction to increase the virtual agent’s accuracy. Module 19: Implementation Methodology Identify the stages of the Google Enterprise Sales Process. Describe the Partner role in the Enterprise Sales Process. Detail the steps in a Contact Center AI project using Google’s ESP. Describe the key activities of the Implementation Phase in ESP. Locate and understand how to use Google’s support assets for Partners. Module 20: Course Review Review what was covered in the course as relates to the objectives.

30.	Company: Google	Category: Google-Analytics		Sub Category: google-analytics-analytics
	Course No.: GCP-600-IN		Course Name: Introduction to Google Ads
	Description:&nbsp: This Google Ads course is designed to introduce students to the world of search engine marketing (SEM) and familiarize them with the Google Ads tool. This Ads Basic course will teach students how to leverage Google Ads to best serve their online marketing needs. The unique feature of this Google Ads training is that it includes hands-on interactive exercises empowering students to productively use Google Ads on their own website right away. To get the most out of class, students are strongly encouraged to have their own Ads account.
	Objectives: After completing the Introduction to Google Ads course, students will be able to: Know the search engine marketing (SEM) Familiarize with the Google Ads tool
	Audience: Online marketers, media analysts, web analysts and business owners who want to promote their businesses using search engine marketing tools.
	Pre Requisites: There are no prerequisites for this course.
	Duration: 1
	Topics: Overview of Search Engine Marketing Introduction of Google Ads Working with Keyboards Writing Ads in Google Ads Creating and Managing your Ads Campaigns Google Ads reporting and account performance Integrating Google Analytics with Google Ads

31.	Company: Google	Category: Google-Analytics		Sub Category: google-analytics-analytics
	Course No.: GCP-605-IN		Course Name: Advanced Google Ads
	Description:&nbsp: This Advance Google Ads course will take your campaigns to the next level. Students will learn how to optimize Ads campaigns by using geo-targeting, dynamic keyword insertion, and other advanced settings. Students will also learn to further integrate Google Ads and Google Analytics accounts to maximize their ability to track the effectiveness of their campaigns.
	Objectives: After completing the Advanced Google Ads course, students will be able to: Optimize Ads campaigns by using geo-targeting, dynamic keyword insertion, and other advanced settings Integrate Google Ads and Google Analytics accounts
	Audience: Those familiar with Google Ads.
	Pre Requisites: There are no prerequisites for this course.
	Duration: 1
	Topics: Advertising on Google’s Display Network Introduction to Google my Business Optimizing your keywords strategy for Google Ads Optimizing Ads campaigns Optimizing Ads Conversion Tracking in Google Ads Leveraging the Google Ads Report in Google Analytics

32.	Company: Google	Category: Google-Analytics		Sub Category: google-analytics-analytics
	Course No.: GCP-610-IN		Course Name: Introduction to Google Analytics
	Description:&nbsp: This Introduction to Google Analytics course is designed to provide students with in-depth knowledge of various features available in Google Analytics and how to leverage them to best serve their business needs. The course begins with introductory chapters to provide a strong foundation to students. This class is different from other Google Analytics training classes in that it includes many hands-on interactive exercises, ensuring that students who complete the course will be able to productively use Google Analytics on their own website right away.
	Objectives: After completing the Introduction to Google Analytics course, students will be able to: Best serve their business needs
	Audience: Web designers, web analysts, online marketers, and general business people.
	Pre Requisites: There are no prerequisites for this course.
	Duration: 1
	Topics: Overview and background of Web Analytics Introduction to Google Analytics Reports in Google Analytics Custom Reporting Advanced Segmentation Understanding filters in Google Analytics Goals in Google Analytics Funnels in Google Analytics Integrating Google Ads with Google Analytics

33.	Company: Google	Category: Google-Analytics		Sub Category: google-analytics-analytics
	Course No.: GCP-615-IN		Course Name: Advanced Google Analytics Training
	Description:&nbsp: This Advanced Google Analytics training course covers some of the more advanced features of Google Analytics, including RegEx, advanced segmentation, intelligence alerts, custom reporting, event tracking, virtual page views, Ecommerce tracking, and custom variables. This Google Analytics training class includes many hands-on interactive exercises, ensuring that students who complete the course will be able to productively use the covered features on their own website right away.
	Objectives: After completing the Advanced Google Analytics Training course, students will be able to: Create a Tag Manager account Enable Ecommerce tracking and reporting Set Up Social Interactions Even Tracking set up
	Audience: While this class is more technical than the Introduction to Google Analytics class, much of the material covered will be interesting to business and marketing people as well as to web developers.
	Pre Requisites: There are no prerequisites for this course.
	Duration: 1
	Topics: Google Tag Manager ECommerce tracking and reporting Social Media Analytics Virtual Pageviews Event Tracking Custom Dimensions and Metrics

1.	Company: Dell	Category: Dell Boomi		Sub Category: Dell Boomi
	Course No.: DEL-115-IN		Course Name: Dell Boomi Integration Developer
	Description:&nbsp: This 5-day interactive virtual class offer hands-on activities to provide immediate practical experience. It is a combination of our Associate and Professional Integration Developer classes in an accelerated format. This class is the best medium to prepare for the Boomi Professional Developer Certification Exam. At Dell Boomi, we want all of our users to develop core competencies in AtomSphere, which we believe lead to sustained success.
	Objectives: After completing our Developer Curriculum you will be able to: Navigate the AtomSphere UI Work with Boomi Documents Configure AtomSphere Connectors (Connection and Operation components) Design complex integration processes to implement advanced logic and process data Design and deploy event-based, web service integration processes Enable administration features for logging and reporting Deploy integration processes to environments Debug and troubleshoot integration processes Translate business scenario into AtomSphere integration process
	Audience: Developers and Architects with understanding of technologies such as SQL, XML, Java Knowledge of enterprise systems (CRM, SFA, ERP, SAP) Experience with Enterprise Integration Tools
	Pre Requisites: Boomi Essentials Course. (4 hr Self-Guided course)
	Duration: 5
	Topics: DEVELOPER 1 Section 1 SaaS Training: Integration Walk-through (Salesforce, Database, Mail Connectors, Query Operations, Advanced Logic, Messaging, Advanced Mapping and Functions). Section 2 Administrator Training for Developers (Deployment, Scheduling, Licensing, Errors and Notifications). Development Life Cycle (Overview, Build Components and Reusability, Change Management). Properties (Dynamic Process Properties, Dynamic Document Properties, Process Property Component). Document Flow (Overview and Concepts, Shape Types, Document Flow). DEVELOPER 2 Section 1 Extensions (Defining configuration settings within your process to be specified at Deployment). REST (Create POST request for generic RESTful web application using Boomi’s HTTP Client Connector). SOAP (Integrate web-based or on premise application which exposes a SOAP web services interface). Process Call (Execute another process from within a process). DEVELOPER 2 Section 2 Business Rules (Implement advanced logic checking multiple business rules to either accept or reject document). Document Caching (Hold/Index frequently used documents in the Atom while performing multiple integrations between documents and process). Try/Catch (Capture process-level or document-level errors for one or more documents which fail during execution). Error Handling (Learn about error handling tools and techniques in Boomi). DEVELOPER 3 Process Route (Select an execution path dynamically at runtime time based on a value such as a document property, data profile, extension value, or trading partner). Web Services (Listen for requests from clients in real time through an embedded web server).. API Management (Enable API publisher to expose versioned APIs for logical groups of APIs). Best Practices (Deployment and Development Framework).

1.	Company: Apple	Category: Apple Training and Certification		Sub Category: Creative
	Course No.: APL-FCPX-101-031-IN		Course Name: Final Cut Pro X 10.4 Professional (e-Learning)
	Description:&nbsp: This Getting Started with Final Cut Pro X course is designed to provide students with the basics of working with Final Cut Pro. It includes a robust set of color correction tools, 360 video capabilities, and solid media management. We’ll cover import and export, and explore some of the key features of the software. This is not a replacement for the Apple certified curriculum, but a self-paced companion course. This course contains interactive lessons that focus on skill growth. The course also contains exercises mirroring the instructor-led version and are performed in a simulated environment that does not require connection or access to a live system which provides extreme flexibility in taking the course. You can start the course at any time within 12 months of enrolling for the course. Once you receive your login credentials, you have 12 months to complete the course. Within these 12 months, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.WEB BASED TRAINING (WBT) IS SELF-DIRECTED AND SELF-PACED. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT AND THE 12 MONTH ACCESS BEGINS ONCE YOU RECEIVE YOUR EMAIL CONFIRMATION WITH LOGIN CREDENTIALS. You are billed for the course when you submit the enrollment form. Web Based Training courses are non-refundable. Once you purchase a Web Based Training course, you will be charged the full price.
	Objectives: This Final Cut Pro X 10.4 Professional course teaches participants the following skills: Understanding the basics of working with Final Cut Pro Importing and exporting project files for collaboration and archive Audio design with Roles Fine-tuning clips in the timeline Comparing alternate shots from a collection of clips Applying effects, transitions and re-timing clips Getting comfortable with key features of the software Using real-world editing scenarios to create a project
	Audience: This Final Cut Pro X 10.4 Professional course is intended for: Filmmakers, Videographers, Technical Directors, Graphic Designers Anyone who wants to edit professional-quality video with Final Cut Pro X and prefers hands-on and interactive instruction Editors with some experience in video production and workflow
	Pre Requisites: Knowledge of OS X and basic computer navigation Basic knowledge of video editing terminology is highly recommended
	Duration: 1
	Topics: Import and Organize How does Final Cut work? A quick tour of the interface Setting up a Library and Event Importing footage graphics and audio Creating Optimized and Proxy Media Changing Clip appearance and ordering Understanding Library Smart Collections and Keywords Creating Keyword Collections Searching for Clips and creating additional Smart Collections Rating Clips Video Editing Creating a Project Adding Clips to the Timeline Navigating the Timeline Basic Editing Tools to Polish Your Edit Adding Connected Clips The Importance of Gap Clips Adding Music Using the Trim Tool Replacing Clips Test Out Clips with Audition Audio Editing Working with Channel Configuration Balancing Primary Audio Mixing in Additional Audio Performing J and L Cuts Changing Volume Over Time with Keyframes Tagging Clips with Roles Displaying Audio Lanes Fixing Common Audio Problems Syncing Separate Video and Audio Transitions, Effects, and Transforming Adding Basic Cross Dissolves Adding and Modifying Additional Transitions Adding Effects to Clips Saving Effect Presets to Save Time Understanding Rendering Basic Speed Changes Stabilizing Footage Manipulating Transform Properties Basic Keyframing Creating Compound Clips Titles and Motion Graphics Working with Custom Text Building a Lower Third Designing a 3D Text Intro The Motion Connection Creating an Animated Title in Motion Create a Logo animation in Motion part 1 Create a Logo Animation Part 2 Color Correction Fixing white Balance with automatic tools Accessing Your Video Scopes Using the Waveform Monitor Using the RGB Parade Using the Vectorscope Fixing Exposure and Color with Color Wheels Adjusting Shots with Color Curves Isolating color with Hue/Saturation Curves Working with color masks and shapes Helpful shortcut keys for color correction Working in HDR Media Management, Exporting, Interoperability Duplicating Projects Exporting a Master File Exporting an H.264 File for Distribution Uploading to Social Media Creating Export Bundles Sending Your Project to Compressor Archiving Your Library Sharing Libraries with Other Users XML and Sharing Projects with Other Users Exporting Captions and Roles 360 Video Understanding 360 Formats Creating a 360 Project Setting Up an HMD (Head mounted Display) Working with the 360 Viewer Understanding the Reorient Tool Working with 360 Blurs and Glows Working with the Patch Effect Working with 360 Titles and Generators

2.	Company: Apple	Category: Apple Training and Certification		Sub Category: End User
	Course No.: APL-iPad-100-IN		Course Name: iPad Basics
	Description:&nbsp: The iPad Basics course covers the fundamentals of an Apple iPad. It is intended for the beginner user who has little experience using their iPad. Students will learn about the home screen, status bar icons, using gestures, searching on your device, and using your Keyboard. Students will learn how to use dictation, multitasking features, and accessing the control center. Students will explore the use of FaceTime video chats and Text Messages. This step-by-step course will also review apps, the App Store, and re-arranging the apps on your home screen.
	Objectives: Upon completion of the iPad Basics course, students will be able to: Customize their iPad, learn important gestures, and understand how to use FaceTime, Text Message, and dictation.
	Audience: This is a beginner level course.
	Pre Requisites:
	Duration: 0.125
	Topics: Overview Home button Sleep/Wake Powering on and off. Volume control Gestures Touch tap Swipe Drag Zoom Flick! Home Screen Status bar WiFi Other important screen icons Accessories Cases Screen protection External Keyboard Spotlight Search How to find anything on your device Search for contacts Navigation Apps Editing your apps The App Store Reviewing & Getting FaceTime Screen overview Contacts Tips Text Messages Screen overview Compose Dictate

3.	Company: Apple	Category: Apple Training and Certification		Sub Category: End User
	Course No.: APL-iPhone-100-IN		Course Name: iPhone Basics
	Description:&nbsp: The iPhone Basics course covers the fundamentals of an iPhone. It is intended for the beginner user who has little experience using their iPhone. Students will learn about the home screen, status bar icons, using gestures, searching on your device, and using your Keyboard. Students will also learn how to use dictation, multitasking features, and accessing the control center. Students will explore making calls and writing/dictating Text Messages. This step-by-step course will also review apps, the App Store, and re-arranging the apps on your home screen.
	Objectives: Upon completion of the iPhone Basics course, students will be able to: Understand how to operate their iPhone, search throughout their device, and gain knowledge about text messages, making calls, and customizing their iPhone.
	Audience: This is a beginner level course.
	Pre Requisites:
	Duration: 0.125
	Topics: Basic Controls Button Features App Icons Settings Apple ID Display Notifications Wallpaper Siri Emergency SOS Apps Editing your apps The App Store Reviewing & Getting Making Calls Favorites Recents Contacts Keypad Voicemail During a call Spotlight Search How to find anything on your device Look for names, type of message, and navigation tools.

4.	Company: Apple	Category: Apple Training and Certification		Sub Category: Creative
	Course No.: APL-LPX-100-IN		Course Name: Absolute Beginner’s Guide to Logic Pro
	Description:&nbsp: This Absolute Beginners Guide to Logic Pro course is designed to provide students with basic foundational knowledge of this exceptional Music Production software.
	Objectives: Upon completion of the Absolute Beginners Guide to Logic Pro course, students will be able to: Understand Logic Pro’s GUI (Graphical User Interface) Navigate the workspace with confidence Learn the Basics of Audio and Midi Have a working knowledge of the Tool Menu Record and Edit tracks
	Audience: Beginners, New to Logic Pro
	Pre Requisites: Logic Pro X Software (Full Install from the App Store)
	Duration: 0.125
	Topics: INTRO Set Up Advanced Settings INPUTS AND OUTPUTS Learn proper routing inside of Logic Pro Key focus and Loops SNAP MODE & NUDGE VALUE Understand the Musical Time Ruler Learn to use snap mode to effectively move regions Edit with precision using nudge LATENCY Learn to tackle this often-misunderstood subject Locate and understand the I/O buffer size behavior UNDERNEATH THE HOOD The core of the program Preference and project settings BASIC WORKFLOW Learn the different kinds of tracks you can use in Logic Pro Master recording in the tracks area Develop your workflow Go over basic audio processing USING CURSOR TOOLS Learn how to use the zoom tool, fade tool, and many others Maximize your workflow by utilizing the primary and secondary tool SUMMING IT UP The advantage of using folders over package files How to save a file How to export a file to WAV or mp3

5.	Company: Apple	Category: Apple Training and Certification		Sub Category: Creative
	Course No.: APL-LPX-125-IN		Course Name: Logic Pro 101
	Description:&nbsp: This Logic Pro 101 course is designed to provide students with the knowledge and skills to navigate Logic Pro like a studio professional.
	Objectives: Upon completion of the Logic Pro 101 course, students will be able to: Navigate and run Logic Pro efficiently and effectively Understand latency Understand MIDI and Drummer Tracks Use Flex Time and Flex Pitch
	Audience: Intermediate and Advanced
	Pre Requisites: Logic Pro X Software (Full Install from the App Store) or any functional D.A.W.
	Duration: 4
	Topics: Navigation in Logic Pro X Navigating Logic Pro Opening and Using the Loop Browser Musical Time Marquee Tool Audio and Sampling Understanding Latency How to Sample in Logic Pro Understanding Smart Tempo Midi & FX Learn what MIDI is and the different ways in which we use MIDI MIDI Workflow Understand MIDI Chase Envelope and Gate Arpeggiator Drummer Understand how a Drummer Track Differs From MIDI and Audio Input Quantization How to Use Track Alternatives Understand Flex Time/Flex Pitch

6.	Company: Apple	Category: Apple Training and Certification		Sub Category: Productivity
	Course No.: APL-MAC101-000-IN		Course Name: Migrating to macOS
	Description:&nbsp: In this course, you will examine the similarities and differences between a Windows Operating System and the Mac Operating System. This course examines where to locate and find files, how to alter settings to best suit your needs and an introduction to the Apple version of applications that are similar to those on a Windows Operating System. It will also examine the features of macOS, working with the dock, organizing files, the system preferences, creating pdf’s, selecting and setting the preferences of printers, working with a network and shortcuts for making your transition easier and a comparison between Windows and Mac Terminology.
	Objectives: Upon completion of the Migrating from PC to Mac course, students will be able to: Navigate the Mac interface to located commands and files Create, save and print documents Set system preferences Use Apple-specific features and commands Make use of Mac applications Manage photos on the Mac Locate Apple support information
	Audience: Anyone migrating from a PC platform to a Mac
	Pre Requisites: Participants should be familiar with using a Windows Operating System. They should be comfortable using the keyboard and mouse. No previous experience with a Mac Operating System is necessary.
	Duration: 0.5
	Topics: macOS Interface The Desktop The Dock Working with the Hard drive Finder View Applications Adding to the Dock Users Organizing your Files Customizing the Finder Sidebar Customizing the Finder Toolbar Toolbar Extras Stacks on the Dock & Desktop Empty the Bin Spotlight File Information Connecting to a Server About your Computer Creating, Saving and Printing Documents Creating a Document Saving, Renaming & Duplicating a Document Printing your Document Creating a PDF Setting Preferences Apple Features QuickLook Screenshots Continuity Camera Mac App Store Siri Comparing Applications Safari vs Chrome Mail vs Outlook Contacts Calendar Pages vs Microsoft Word Numbers vs Microsoft Excel Photos Support Mac Help Shortcuts for macOS Windows vs Mac Terminology

7.	Company: Apple	Category: Apple Training and Certification		Sub Category: Productivity
	Course No.: APL-MAC101-005-IN		Course Name: Welcome to Mac
	Description:&nbsp: This Welcome to Mac course covers the fundamentals of macOS, iLife, iWork and iCloud. It is intended for the new user who has no prior Mac experience. Students will explore macOS, Photo Booth, Photos, GarageBand and iMovie, and create text, PDF, audio and video files. Next, students will explore Pages, Numbers and Keynote, and create word processor, spreadsheet and presentation files. This course is a step-by-step approach to learning the Mac platform.
	Objectives: Upon completion of the Welcome to Mac course, students will be able to: Configure the Mac using System Preferences Navigate the macOS user interface Create and manipulate files Explore useful utilities Create PDF, audio and video files using Photo Booth, Photos, GarageBand and iMovie Create word processor, spreadsheet and presentation files using Pages, Numbers and Keynote
	Audience: New Mac users with no prior Mac experience
	Pre Requisites: None
	Duration: 1
	Topics: SIMPLE macOS GUIDE Hardware Controls: System Preferences Creating User Accounts Energy Saver Trackpad Desktop and Screen Saver Network Printers Keyboard Shortcuts The Mac Desktop The Menu Bar Main Desktop Access Gestures The Dock Customising the Dock Running and Quitting Apps Managing Stacks and Trash The Finder Window Finder Toolbar Sidebar Manipulating Windows Spotlight Searching File Operations Creating Documents Duplicating, Renaming and Moving Documents Deleting Documents Sharing Documents Gestures Review Utilities Managing Files Sorting and arranging documents/files Smart folders and advanced search More File Operations Review Locking documents Browsing versions Other Applications to be explored Automator Clean up (optional) SIMPLE ILIFE GUIDE Photo Booth QuickTime Screen Recording Photos GarageBand iMovie SIMPLE IWORK GUIDE Pages Numbers Keynote Setup requirements: A Mac device that has been freshly installed with the latest version of OS 10.15 Catalina, iLife apps, and iWork apps. It is not recommended to use a Mac with the user’s personal data on it, otherwise a new user account will need to be created for this training.

8.	Company: Apple	Category: Apple Training and Certification		Sub Category: macOS
	Course No.: APL-MAC101-111-IN		Course Name: macOS Support Essentials 11 (Big Sur)
	Description:&nbsp: macOS Support Essentials 11, is a top-notch primer for anyone who needs to support, troubleshoot, or optimize macOS Big Sur, such as IT professionals, technicians, help desk specialists, and ardent Mac users. This is the only Apple Pro Training Series course that covers Big Sur. Students will find in-depth, step-by-step instructions on everything from upgrading, updating, reinstalling and configuring macOS Big Sur to setting-up network services like the Content Caching service.
	Objectives: What you’ll learn: System utilities and new features in macOS Big Sur, including security and privacy enhancements, Control Center and Notification Center, Safari, system extensions, macOS Recovery, Startup Security Utility, and the Signed System Volume (SSV).
	Audience: Anyone who needs to support, troubleshoot, or optimize macOS Big Sur, such as IT professionals, technicians, help desk specialists, and ardent Mac users.
	Pre Requisites: Recommended knowledge: macOS familiarity Basic computer navigation skills
	Duration: 3
	Topics: Installation and Configuration Introduction to macOS Update, Upgrade, or Reinstall macOS Set Up and Configure macOS Use the Command Line Use macOS Recovery Update macOS User Accounts Manage User Accounts Manage User Home Folders Manage Security and Privacy Manage Password Changes File Systems Manage File Systems and Storage Manage FileVault Manage Permissions and Sharing Use Hidden Items, Shortcuts, and File Archives Data Management Manage System Resources Use Metadata, Spotlight, and Siri Manage Time Machine Apps and Processes Install Apps Manage Files Manage and Troubleshoot Apps Network Configuration Manage Basic Network Settings Manage Advanced Network Settings Troubleshoot Network Issues Network Services Manage Network Services Manage Host Sharing and Personal Firewall System Management Troubleshoot Peripherals Manage Printers and Scanners Troubleshoot Startup and System Issues

9.	Company: Apple	Category: Apple Training and Certification		Sub Category: macOS
	Course No.: APL-mac150-150-IN		Course Name: Mac Integration Basics 10.15
	Description:&nbsp: Organizations are increasingly integrating Mac computers into Windows or other standards-based network environments. But users and the IT professionals who support them can relax, because Mac integration is easy.
	Objectives: What you’ll learn: Integrate a Mac into a Windows network environment. Configure a Mac to work with Active Directory. Take advantage of network services, file sharing, printing, instant messaging, email, calendars, and contacts. Provide security at the user, local-networking, and remote-networking levels. Migrate data from a Windows computer to a Mac. Back up data. Run Windows programs on a Mac.
	Audience: Who should attend: Users who bring a Mac into organizations that predominantly use the Microsoft Windows operating system and Windows Server Essentials Users who replace a Windows computer with a Mac IT professionals who support Mac users in organizations that predominantly use Windows and Windows Server Essentials
	Pre Requisites: To have the best learning experience with this course, you should understand how to use a Mac, a Windows computer, and computer peripherals.
	Duration: 0.5
	Topics: Directory Services Connect a Mac to an Active Directory server. Share Files Connect to file servers, turn on personal file sharing. Configure Collaborative Services Manage Internet Accounts preferences, connect to an Exchange Server, connect Mail to non-Windows servers, add accounts in Mail, Contacts, and Calendars. Secure a Mac Built-In Security features, create strong passwords, use two-factor authentication, set a firmware password, lock a Mac screen, create user accounts, disable automatic login, protect start-up disk files, ensure that the apps you download are safe, provide network security. Print Connect to a local printer, connect to, share, and print from network printers. Move and Back Up Content Move content, back up content. Run Windows on a Mac Run Windows natively or virtually, Microsoft Office for macOS, cross-platform apps, cross-platform files.

10.	Company: Apple	Category: Apple Training and Certification		Sub Category: App Development
	Course No.: APL-SWIFT-101-IN		Course Name: Introduction to Programming in Swift 5 (e-Learning)
	Description:&nbsp: This Introduction to Programming in Swift 5 course is designed to provide students with the absolute basics of the Swift programming language. Whether you are a brand new programmer or have experience with other programming languages this course is for you.Some of the things you will learn in this course are: An Introduction to Swift 5 programming concepts Installing the necessary tools Working with data such as Integers and Strings Creating reusable code with functions Working with data constructs such as arrays and dictionaries Object-oriented programming Model View Controller
	Objectives: Upon completion of the Introduction to Programming in Swift 5 course, students will be able to: install Xcode and other Swift tools to properly set up your development environment program using Swift including strings, variables, constants and program logic use data constructs and create reusable code segments build common architectures for Swift and iOS development
	Audience: Anyone who wants to learn Swift 5 programming. No prior experience is required.
	Pre Requisites: Basic understanding of computers and applications
	Duration: 1.4
	Topics: Installation, Setup and Your First Code Downloading and installing Xcode Hello Swift Variables, Strings and Numbers Variables Working with strings Working with numbers Conditional Logic, Arrays and Loops Boolean and conditional logic Constants and logical operators Arrays Loops Dictionaries, Functionals and Optionals Dictionaries Functionals in Swift Optionals Architecture and Object-Oriented Programming Object-oriented programming Inheritance Polymorphism MVC in theory Creating an Xcode project Project groups for MVC Creating a model layer Creating a custom view layer Connecting view to controller Securing model layer MVC challenge

11.	Company: Apple	Category: Apple Training and Certification		Sub Category: App Development
	Course No.: APL-SWIFT-110-IN		Course Name: iOS Application Development with Swift 5 (e-Learning)
	Description:&nbsp: This iOS APPLICATION DEVELOPMENT WITH SWIFT 5 course is designed to provide students use their skills in Swift 5 to develop iOS applicationsSome of the things you will learn in this course are: Write the code to build your very first iOS application Manage screen display with multiple views Use auto layout and the interface builder Create applications with user interaction Design a user interface allowing for multiple screen size and direction Write and execute unit tests to keep your code error-free Perform various calculations using Swift By the end of this course you will know how to build simple iOS applications and you’ll be ready to move on and learn about using tables and data in iOS.
	Objectives: Upon completion of the iOS APPLICATION DEVELOPMENT WITH SWIFT 5 course, students will be able to: Create a new iOS project and build your first iOS app Manage screen displays and group multiple views Use programming commands to allow for user interaction Write and execute unit tests to keep your code error free
	Audience: Anyone with basic knowledge of Swift 5 who wants to build iOS apps
	Pre Requisites: Basic understanding of Swift 5 programming, such as from the LearnQuest course Introduction to Programming in Swift 5
	Duration: 1.5
	Topics: Building Your First App Introduction Differences in Xcode versions Building your first iOS app Xcode features Swoosh App: Introduction to Interface Builder Creating the Welcome screen Working with iOS frames iOS auto layout basics Working with UIStackViews Introduction to Segues on iOS Renaming view controllers Debugging crashes on iOS Programmatic Segues on iOS IBActions on iOS Passing data between controllers Supporting iPhones and iPads iOS size classes Supporting iPhone and iPad Shape maker project Window Shopper Your First Fully Functional App Custom TextFields iOS Input Accessory and IBDesignable Writing unit tests for Creating calculations Custom drawing Unit converter app

12.	Company: Apple	Category: Apple Training and Certification		Sub Category: App Development
	Course No.: APL-SWIFT-120-IN		Course Name: Tables, Data & Networking in iOS (e-Learning)
	Description:&nbsp: In this TABLES, DATA & NETWORKING IN iOS course you will learn how to work with data in iOS. Data is the key ingredient for any functional application, and one must learn how to properly display it to the user. You will learn how to use tables and collection views to display data to users, and how to use Core Data to build more robust, data-driven applications.Every app must have data. Making web requests is by far the most common thing you will do as an iOS developer. You will learn how to get your apps working with servers and external data. Throughout the course, you will create a ToDo app, including learning how to decode data from a web server as well as how to make POST requests.Some of the things you will learn in this course are: How to create memory-efficient tables and display data in a list How to persist and fetch data How to model data and create database relationships How to decode web server data and make POST requests
	Objectives: Upon completion of the TABLES, DATA & NETWORKING IN iOS course, students will be able to: Work with data in your iOS application Create robust, data-driven applications with persistent data Make your applications work with servers and external data Use APIs and communicate with web servers
	Audience: Anyone who wants to expand their programming knowledge using Swift 5.
	Pre Requisites: Some programming experience with Swift5
	Duration: 1.5
	Topics: Working with Tables Using delegates Implementing protocols Creating memory-efficient tables Display data in a list Implement UITableView Implement UICollectionView Working with Core Data Modeling data for Core Data Creating Database Relationships Persisting Data Fetching Data Performing Data Updates Network Requests and APIs How APIs Work Making Network Requests Communicating with Web Servers Decoding, Async & POST Requests Synchronous vs Asynchronous Parsing and Decoding JSON from the Server Making POST Requests and Sending Data to a Server

13.	Company: Apple	Category: Apple Training and Certification		Sub Category: App Development
	Course No.: APL-SWIFT-130-IN		Course Name: iOS App Store & In-App Purchases (e-Learning)
	Description:&nbsp: In this iOS APP STORE & IN-APP PURCHASES course you will learn the basics of monetization on iOS, starting with in-app ads and then moving to in-app purchases. You will learn more advanced in-app purchase integrations, and you’ll make both consumable and non-consumable purchases while building a realistic iOS app. Finally, you will learn how to take your apps even better with in-app subscriptions, and how to start and cancel subscriptions and create a delightful user experience.
	Objectives: Upon completion of the iOS App Store & In-App Purchases course, students will be able to: Create and monetize apps on the Apple App Store, including in-app advertisements and purchases Create consumable and non-consumable in-app purchases Create a user interface that respects purchases, and prepare to publish on iTunes Connect Create and manage user subscriptions
	Audience: Anyone who wants to expand their programming knowledge using Swift 5.
	Pre Requisites: Some programming experience with Swift5
	Duration: 1
	Topics: Simple In-App Purchases Create apps on the Apple App Store Create in-app advertisements Create in-app purchase tiers Restore in-app purchases Consumables & Non-Consumables Create consumable in-app purchases Create non-consumable in-app purchases Restore in-app purchases Handle success and failures with in-app purchases Create user-interface that respects purchases Create an app on iTunes Connect and prepare to publish In-App Purchase Subscriptions Create subscription tiers Check for failed or cancelled subscriptions Subscribe a user in the app Handle successes and failures

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