Google Cloud Professional Cloud Architect(PCA) – Learning Path

The below learning path courses are available in both Plural sight and Coursera.

Google Cloud Fundamentals: Core Infrastructure

Module 2: Getting Started with Google Cloud Platform

  • Identify the purpose of projects on Google Cloud Platform.
  • Understand the purpose of and use cases for Identity and Access Management.
  • List the methods of interacting with Google Cloud Platform.
  • Lab: Getting Started with Google Cloud Platform.

Module 3: Google Compute Engine and Networking

  • Identify the purpose of and use cases for Google Compute Engine.
  • Understand the basics of networking in Google Cloud Platform.
  • Lab: Deploying Applications Using Google Compute Engine.

Module 4: Google Cloud Platform Storage Options

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

Module 5: Google Container Engine

  • Define the concept of a container and identify uses for containers.
  • Identify the purpose of and use cases for Google Container Engine and Kubernetes.
  • Introduction to Hybrid and Multi-Cloud computing (Anthos).
  • Lab: Deploying Applications Using Google Container Engine.

Module 6: Google App Engine and Google Cloud Datastore

  • Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore.
  • Contrast the App Engine Standard environment with the App Engine Flexible environment.
  • Understand the purpose of and use cases for Google Cloud Endpoints.
  • Lab: Deploying Applications Using App Engine and Cloud Datastore.

Module 7: Deployment and Monitoring

  • Understand the purpose of template-based creation and management of resources.
  • Understand the purpose of integrated monitoring, alerting, and debugging.
  • Lab: Getting Started with Stackdriver and Deployment Manager.

Module 8: Big Data and Machine Learning

  • 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.

Architecting with Google Compute Engine

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.

Architecting with Google Cloud: Design and Process

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.

Getting started with Google Kubernetes Engine

Module 2: Containers and Kubernetes in Google Cloud

  • 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 Google Cloud Compute platforms.

Module 3: Kubernetes Architecture

  • Understand the architecture of Kubernetes: pods, namespaces.
  • Understand the control-plane components of Kubernetes.
  • Create container images using Cloud Build.
  • Store container images in Container Registry.
  • Create a Kubernetes engine cluster.

Module 4: Introduction to Kubernetes Workloads

  • The kubectl command.
  • Introduction to deployments.
  • Pod networking.
  • Volumes overview.
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