odule 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 the purpose of and use cases for Identity and Access Management.
• List the methods of interacting with Google Cloud.
• Lab: Getting Started with Cloud Marketplace.

Module 3: Virtual Machines in the Cloud

• Identify the purpose of and use cases for Google Compute Engine.
• Understand the basics of networking in Google Cloud.
• Lab: Getting Started with Google Compute Engine.

Module 4: Storage in the Cloud

• Understand the purpose of and use cases for: Cloud Storage, Cloud Bigtable, Cloud SQL, Cloud Spanner, and Firestore.
• Learn how to choose between the various storage options on Google Cloud.
• Lab: Getting Started with Cloud Storage and Cloud SQL.

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 Kubernetes Engine and Kubernetes.
• Introduction to Hybrid and Multi-Cloud computing (Anthos).
• Lab: Getting Started with Kubernetes Engine.

Module 6: Applications in the Cloud

• Understand the purpose of and use cases for App Engine.
• Contrast the App Engine standard environment with the App Engine flexible environment.
• Understand the purpose of and use cases for Cloud Endpoints.
• Lab: Getting Started with App Engine.

Module 7: Containers in the Cloud

• Understand how Cloud Source Repositories, Cloud Functions, and Deployment Manager support development in the cloud.
• Understand the purpose of integrated monitoring, alerting, and debugging.
• Lab: Getting Started with Deployment Manager and Cloud 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.
• Lab: Getting Started with BigQuery.

Module 9: Summary and Review

• Summary and Review.
• What’s Next?.

Module 1: 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.

Module 2: 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.

Module 3: 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.

Module 4: 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.

Module 5: 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.

Module 6: 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.

Module 7: 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.

Module 8: 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.

Module 9: 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.

Module 10: Infrastructure Modernization

• Automate the deployment of Google Cloud services using Deployment Manager or Terraform.
• Outline the Google Cloud Marketplace.

Module 11: Managed Services

• Describe the managed services for data processing in Google Cloud.

Module 1: Introduction to Google Cloud

• Identify Google Cloud services and their function.
• Choose the right Google Cloud services to create your own Cloud solution.

Module 2: Containers and Kubernetes in Google Cloud

• Create a container using Cloud Build.
• Store a container in Container Registry.
• Compare and contrast Kubernetes and GKE features.

Module 3: Kubernetes Architecture

• Conceptualize the Kubernetes architecture.
• Deploy a Kubernetes cluster using GKE.
• Deploy pods to a GKE cluster.
• View and manage Kubernetes objects.
• Conceptualize the migrate for Anthos process.

Module 4: Kubernetes Operations

• Work with the Kubectl command.
• Inspect the cluster and pods.
• View a Pod’s console output.
• Sign in to a pod interactively.

Module 5: Deployment, 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.

Module 6: 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.

Module 7: 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.

Module 8: Access Control and Security in Kubernetes and Kubernetes Engine

• Define IAM roles and policies for GKE.
• Define Kubernetes RBAC roles and role bindings.
• Define Kubernetes pod security policies.

Module 9: Logging and Monitoring

• Create forensic logs for systems monitoring.
• Monitor your system performance from different vantage points.
• Create probes for wellness checks on live applications.

Module 10: Using Google Cloud Managed Storage Services from Kubernetes Applications

• Understand use cases for Cloud Storage within a Kubernetes application.
• Understand use cases for Cloud SQL and Cloud Spanner within a Kubernetes application.
• Understand use cases for Datastore within a Kubernetes application.
• Understand use cases for Cloud Bigtable within a Kubernetes application.

Module 11: Logging and Monitoring

• Create a continuous delivery pipeline using Cloud Build 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.

Module 1: Defining the Service

• Describe users in terms of roles and personas.
• Write qualitative requirements with user stories.
• Write quantitative requirements using key performance indicators (KPIs).
• Evaluate KPIs using SLOs and SLIs.
• Determine the quality of application requirements using SMART criteria.

Module 2: Microservice Design and Architecture

• Decompose monolithic applications into microservices.
• Recognize appropriate microservice boundaries.
• Architect stateful and stateless services to optimize scalability and reliability.
• Implement services using 12-factor best practices.
• Build loosely coupled services by implementing a well-designed REST architecture.
• Design consistent, standard RESTful service APIs.

Module 3: DevOps Automation

• Automate service deployment using CI/CD pipelines.
• Leverage Cloud Source Repositories for source and version control.
• Automate builds with Cloud Build and build triggers.
• Manage container images with Google Container Registry.
• Create infrastructure with code using Deployment Manager and Terraform.

Module 4: Choosing Storage Solutions

• Choose the appropriate Google Cloud data storage service based on use case, durability, availability, scalability and cost.
• Store binary data with Cloud Storage.
• Store relational data using Cloud SQL and Spanner.
• Store NoSQL data using Firestore and Cloud Bigtable.
• Cache data for fast access using Memorystore.
• Build a data warehouse using BigQuery.

Module 5: Google Cloud and Hybrid Network Architecture

• Design VPC networks to optimize for cost, security, and performance.
• Configure global and regional load balancers to provide access to services.
• Leverage Cloud CDN to provide lower latency and decrease network egress.
• Evaluate network architecture using the Cloud Network Intelligence Center.
• Connect networks using peering and VPNs.
• Create hybrid networks between Google Cloud and on-premises data centers using Cloud Interconnect.

Module 6: Deploying Applications to Google Cloud

• Choose the appropriate Google Cloud deployment service for your applications.
• Configure scalable, resilient infrastructure using Instance Templates and Groups.
• Orchestrate microservice deployments using Kubernetes and GKE.
• Leverage App Engine for a completely automated platform as a service (PaaS).
• Create serverless applications using Cloud Functions.

Module 7: Designing Reliable Systems

• Design services to meet requirements for availability, durability, and scalability.
• Implement fault-tolerant systems by avoiding single points of failure, correlated failures, and cascading failures.
• Avoid overload failures with the circuit breaker and truncated exponential backoff design patterns.
• Design resilient data storage with lazy deletion.
• Analyze disaster scenarios and plan for disaster recovery using cost/risk analysis.

Module 8: Security

• Design secure systems using best practices like separation of concerns, principle of least privilege, and regular audits.
• Leverage Cloud Security Command Center to help identify vulnerabilities.
• Simplify cloud governance using organizational policies and folders.
• Secure people using IAM roles, Identity-Aware Proxy, and Identity Platform.
• Manage the access and authorization of resources by machines and processes using service accounts.
• Secure networks with private IPs, firewalls, and Private Google Access.
• Mitigate DDoS attacks by leveraging Cloud DNS and Cloud Armor.

Module 9: Maintenance and Monitoring

• Manage new service versions using rolling updates, blue/green deployments, and canary releases.
• Forecast, monitor, and optimize service cost using the Google Cloud pricing calculator and billing reports and by analyzing billing data.
• Observe whether your services are meeting their SLOs using Cloud Monitoring and Dashboards.
• Use Uptime Checks to determine service availability.
• Respond to service outages using Cloud Monitoring Alerts.

Module 1: Understanding the Professional Cloud Architect Certification

• 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

Module 2: Sample Case Studies

• MountKirk Games
• Dress4Win
• TerramEarth

Module 3: Designing and Implementing

• 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

Module 4: Optimizing and Operating

• 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

Module 5: Next Steps

• 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