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Google Cloud Exam Syllabus

Professional Cloud DevOps Engineer syllabus, skills measured, and exam topics

A Professional Cloud DevOps Engineer implements processes and capabilities throughout the systems development lifecycle using Google Cloud-recommended methodologies and tools. They enable efficient software and infrastructure delivery while balancing reliability with delivery

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Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
Section 1: Bootstrapping and maintaining a Google Cloud organization 20%
Section 3: Applying site reliability engineering practices 18%
Section 5: Optimizing performance and cost 12%

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

Section 1: Bootstrapping and maintaining a Google Cloud organization (~20% of

  • the exam)
  • 1.1 Designing the overall resource hierarchy for an organization. Considerations include:
  • Organizing resources (e.g., application-centric, projects, folders)
  • Shared networking (e.g., Shared VPC, VPC Network Peering, Private Service Connect)
  • Multi-project monitoring and logging
  • Identity and Access Management (IAM) roles and organization-level policies
  • Creating and managing service accounts
  • Data residency
  • 1.2 Managing infrastructure. Considerations include:
  • Infrastructure-as-code tooling and managed services (e.g., Infrastructure Manager,
  • Cloud Foundation Toolkit, Config Connector, GitOps, Terraform, Helm)
  • Making infrastructure changes using Google-recommended practices and blueprints

Section 2: Building and implementing CI/CD pipelines, including continuous

  • testing, for application, infrastructure, and machine learning workloads (~25% of
  • the exam)
  • 2.1 Designing pipelines. Considerations include:
  • CI/CD of applications and infrastructure
  • Artifact management with Artifact Registry
  • Deployment to hybrid and multi-cloud environments (e.g., GKE)
  • CI/CD pipeline triggers
  • Configuring deployment processes (e.g., approval flows)
  • 2.2 Implementing and managing pipelines. Considerations include:
  • Auditing and tracking deployments (e.g., Artifact Registry, Cloud Build, Cloud Deploy,
  • Cloud Audit Logs)
  • Deployment strategies (e.g., canary, blue/green, rolling, traffic splitting, feature flags)

Section 3: Applying site reliability engineering practices (~18% of the exam)

  • 3.1 Balancing change, velocity, and reliability of the service. Considerations include:
  • Defining SLIs (e.g., availability, latency), SLOs, and SLAs
  • Error budgets (e.g., Cloud Service Mesh definitions)
  • Opportunity cost of risk and reliability (e.g., number of “nines”)
  • 3.2 Managing service lifecycle. Considerations include:
  • Service management (e.g., planning, deployment, maintenance, retirement)
  • Capacity planning (e.g., quotas, limits, reservations, Dynamic Workload Scheduler)
  • Autoscaling (e.g., managed instance groups, Cloud Run, GKE)
  • 3.3 Mitigating incident impact on users. Considerations include:
  • Draining/redirecting traffic
  • Adding capacity
  • Rollback strategies

Section 4: Implementing observability practices and troubleshooting issues

  • (~25% of the exam)
  • 4.1 Instrumenting and collecting telemetry. Considerations include:
  • Collecting and importing logs (e.g., Ops Agent, OpenTelemetry, Cloud Audit Logs, VPC
  • Flow Logs, Cloud Service Mesh)
  • Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source
  • considerations)
  • Collecting metrics (e.g., from applications, platforms, networking, Cloud Service Mesh,
  • Google Cloud Managed Service for Prometheus, hybrid/multi-cloud environments)
  • Creating synthetic monitors to proactively probe application endpoints and workflows
  • Creating custom metrics, including log-based metrics
  • 4.2 Managing and analyzing logs. Considerations include:
  • Analyzing logs using the Logs Explorer and the Logging query language

Section 5: Optimizing performance and cost (~12% of the exam)

  • 5.1 Collecting performance information in Google Cloud. Considerations include:
  • Application performance monitoring
  • Active Assist insights and recommendations
  • 5.2 Implementing FinOps practices for optimizing resource utilization and costs. Considerations
  • include:
  • Observability costs
  • Spot virtual machines (VMs)
  • Optimizing resource usage for cost and efficiency
  • Infrastructure cost planning (e.g., committed-use discounts, sustained-use discounts,
  • network tiers)
  • Leveraging Google Cloud recommenders (e.g., cost, security, performance,
  • manageability, reliability)