In Google Cloud Platform Fundamentals: Core Infrastructure, you will learn
Module 1: Introducing Google Cloud Platform
- Explain the advantages of Google Cloud Platform.
- 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 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: Virtual Machines and Networks in the Cloud
- Identify the purpose of and use cases for Google Compute Engine.
- Understand the various Google Cloud Platform networking and operational tools and services.
- Lab: Compute Engine
Module 4: Storage in the Cloud
- Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, Google Cloud Big table, and Google Cloud Datastore.
- Learn how to choose between the various storage options on Google Cloud Platform.
- Lab: 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.
- Lab: Kubernetes Engine
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 the purpose of and use cases for Google Cloud Endpoints.
- Lab: App Engine
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 the purpose of integrated monitoring, alerting, and debugging.
- Lab: Deployment Manager and Stackdriver
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: BigQuery
Architecting with Google Compute Engine includes presentations, demonstrations, and hands-on labs.
Module 1: Introduction to Google Cloud Platform
- Google Cloud Platform (GCP) Infrastructure
- Using GCP
- Lab: Console and Cloud Shell
- Demo: Projects
- Lab: Infrastructure Preview
Module 2: Virtual Networks
- Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, Firewall rules
- Subnetworks for resource management instead of physical network topology
- Lab: Virtual Networking
- Lab: Bastion Host
Module 3: Virtual Machines
- Compute Engine
- Lab: Creating Virtual Machines
- Compute options (vCPU and Memory)
- Images
- Common Compute Engine actions
- Lab: Working with Virtual Machines
Module 4: Cloud IAM
- Organizations, Roles, Members, Service accounts, Cloud IAM best practices
- Lab: Cloud IAM
Module 5: Data Storage Services
- Cloud Storage
- Lab: Cloud Storage
- Cloud SQL
- Lab: Cloud SQL
- Cloud Spanner, Cloud Datastore
- Lab: Cloud Datastore
- Cloud Bigtable
Module 6: Resource Management
- Cloud Resource Manager, Quotas, Labels, Names, Billing
- Demo: Billing Administration
- Lab: Examining Billing Data with BigQuery
Module 7: Resource Monitoring
- Stackdriver, Monitoring
- Lab: Resource Monitoring (Stackdriver)
- Logging, Error Reporting, Tracing, Debugging
- Lab: Error Reporting and Debugging (Stackdriver)
Module 8: Interconnecting Networks
- Cloud Virtual Private Network (VPN)
- Lab: Virtual Private Networks (VPN)
- Cloud Router, Cloud Interconnect, External Peering, Cloud DNS
Module 9: Load Balancing
- Managed Instance Groups, HTTPS load balancing, Cross-region and content-based load balancing, SSL proxy/TCP proxy load balancing, Network load balancing
- Lab: VM Automation and Load Balancing
Module 10: Autoscaling
- Autoscaling, Policies, Configuration
- Lab: Autoscaling
Module 11: Infrastructure Automation with Google Cloud Platform APIs
- Infrastructure automation, Images, Metadata, Scripts, Google Cloud API
- Lab: Google Cloud Platform API Infrastructure Automation
Module 12: Infrastructure Automation with Deployment Manager
- Deployment Manager, Configuration, Cloud Launcher
- Lab: Deployment Manager
Module 13: Managed Services
- Cloud Dataproc, Cloud Dataflow, BigQuery, Cloud Datalab
Module 14: Application Infrastructure Services
- Cloud Pub/Sub, API Management, Cloud Functions, Cloud Source Repositories, Specialty APIs
Module 15: Application Development Services
Module 16: Containers
- Containers, Kubernetes Engine, Container Registry
- Lab: Kubernetes Load Balancing
- Kubernetes Engine, App Engine, or Containers on Compute Engine?
Architecting with Google Kubernetes Engine teaches you :
Module 1: 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
Module 2, 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
Module 3: 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
Module 4: Kubernetes Operations
- Work with the kubectl command
- Inspect the cluster and Pods
- View a Pods console output
- Sign in to a Pod interactively
Module 5: 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
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
- 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
Module 9: 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
Module 10: 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
Architecting with Google Cloud Platform: Design and Process teaches you
Module 1: Defining the Service
- Design in this class.
- State and solution.
- Measurement.
- Gathering requirements, SLOs, SLAs, and SLIs (key performance indicators).
Module 2: Business-logic layer design
- Microservices architecture.
- GCP 12-factor support.
- Mapping compute needs to Google Cloud Platform processing services.
- Compute system provisioning.
Module 3: Data layer design
- Classifying and characterizing data.
- Data ingest and data migration.
- Identification of storage needs and mapping to Google Cloud Platform storage systems.
Module 4: Network 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.
Module 5: 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.
Module 6: 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.
Module 7: Capacity planning and cost optimization
- Capacity planning.
- Pricing.
Module 8: Deployment, monitoring and alerting, and incident response
- Deployment.
- Monitoring and alerting.
- Incident response.
In Preparing for the Professional Cloud Architect Examination, you will learn
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