Google Professional Cloud DevOps Engineer Certification Path Introduction

Section 1. Applying site reliability engineering principles to a service

Balance change, velocity, and reliability of the service:

• Discover SLIs (e.g., availability, latency)

• Define SLOs and understand SLAs

• Agree to consequences of not meeting the error budget

• Construct feedback loops to decide what to build next

• Eliminate toil via automation
   

Manage service life cycle:

• Manage a service (e.g., introduce a new service, deploy, maintain, and retire it)

• Plan for capacity (e.g., quotas and limits management)
   

Ensure healthy communication and collaboration for operations:

• Prevent burnout (e.g., set up automation processes to prevent burnout)

• Foster a learning culture

• Foster a culture of blamelessness
  
   

Section 2. Building and implementing CI/CD pipelines for a service

Design CI/CD pipelines:

• Creating and storing immutable artifacts with Artifact Registry

• Deployment strategies with Cloud Build and Spinnaker

• Deployment to hybrid and multicloud environments with Anthos, Spinnaker, and Kubernetes

• Artifact versioning strategy with Cloud Build and Artifact Registry

• CI/CD pipeline triggers with Cloud Source Repositories, external SCM, and Pub/Sub

• Testing a new version with Spinnaker

• Configuring deployment processes (e.g., approval flows)
   

Implement CI/CD pipelines:

• CI with Cloud Build

• CD with Cloud Build

• Open source tooling (e.g., Jenkins, Spinnaker, GitLab, Concourse)

• Auditing and tracing of deployments (e.g., CSR, Artifact Registry, Cloud Build, Cloud Audit Logs)
   

Manage configuration and secrets:

• Secure storage methods

• Secret rotation and config changes
   

Manage infrastructure as code:

• Infrastructure code versioning

• Make infrastructure changes safer

• Immutable architecture
   

Deploy CI/CD tooling:

• Centralized tools vs. multiple tools (single vs. multi-tenant)

• Security of CI/CD tooling
   

Manage different development environments (e.g., staging, production):

• Decide on the number of environments and their purpose

• Create environments dynamically per feature branch with GKE

• Local development environments with Docker, Cloud Code, Skaffold
   

Secure the deployment pipeline:

• Vulnerability analysis with Artifact Registry

• Binary Authorization

• IAM policies per environment
  
   

Section 3. Implementing service monitoring strategies

Manage application logs:

• Collecting logs from Compute Engine, GKE with Cloud Logging, Fluentd

• Collecting third-party and structured logs with Cloud Logging, Fluentd

• Sending application logs directly to the Cloud Logging API
   

Manage application metrics with Cloud Monitoring:

• Collecting metrics from Compute Engine

• Collecting GKE/Kubernetes metrics

• Use Metrics Explorer for ad hoc metric analysis
   

Manage Cloud Monitoring platform:

• Creating a monitoring dashboard

• Filtering and sharing dashboards

• Configure third-party alerting in Cloud Monitoring (e.g., PagerDuty, Slack)

• Define alerting policies based on SLIs with Cloud Monitoring

• Automate alerting policy definition with Terraform

• Implementing SLO monitoring and alerting with Cloud Monitoring

• Understand Cloud Monitoring integrations (e.g., Grafana, BigQuery)

• Using SIEM tools to analyze audit/flow logs (e.g., Splunk, Datadog)

• Design Cloud Monitoring metrics scopes