Google Cloud Professional Cloud DevOps EngineerStudy guide
The professional certification for delivering CI/CD, reliability, observability, and cost optimization using SRE and platform-engineering practices (Professional Cloud DevOps Engineer).
About Google Cloud Professional Cloud DevOps Engineer (GCP-PCDO)
Google Cloud Professional Cloud DevOps Engineer (GCP-PCDO) is a Professional / Expert-level certification from Google Cloud. This page organizes the exam scope into a 5-chapter, 10-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Bootstrapping and maintaining a Google Cloud organization~20%
- Building and implementing CI/CD pipelines, including continuous testing~25%
- Applying site reliability engineering practices~18%
- Implementing observability practices and troubleshooting issues~25%
- Optimizing performance and cost~12%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://cloud.google.com/learn/certification/cloud-devops-engineer
1Bootstrapping and maintaining a Google Cloud organization
- 1.1Resource hierarchy, IAM, and shared networking
Understand designing an org resource hierarchy (organization/folders/projects), IAM roles and organization-level policies, creating and managing service accounts, shared networking via Shared VPC/VPC Network Peering/Private Service Connect, multi-project monitoring/logging, and data residency.
- 1.2Infrastructure as Code and managing environments/dev environments
Understand IaC tooling and managed services (Infrastructure Manager, Config Connector, Cloud Foundation Toolkit, GitOps, Terraform, Helm), making changes per blueprints, multi-environments such as staging/production (ephemeral environments, GKE fleets, safe patching/upgrades), and secure cloud development environments (Cloud Workstations, Cloud Shell, Gemini Code Assist).
2Building and implementing CI/CD pipelines, including continuous testing
- 2.1Designing CI/CD pipelines and deployment strategies
Understand continuous integration (CI) with Cloud Build, continuous delivery (CD) with Cloud Deploy (Skaffold/Kustomize), artifact management with Artifact Registry, deployment to hybrid/multi-cloud (GKE), pipeline triggers and approval flows, and deployment strategies (canary/blue-green/rolling/traffic splitting/feature flags) with success metrics.
- 2.2Securing the deployment pipeline and managing secrets
Understand auditing/tracking deployments (Artifact Registry, Cloud Build, Cloud Deploy, Cloud Audit Logs), key management (Cloud KMS), secret management (Secret Manager, Workload Identity Federation) with build/runtime injection, Artifact Analysis and vulnerability scanning, software supply-chain security (Binary Authorization, SLSA), and environment-based IAM policies.
3Applying site reliability engineering practices
- 3.1SLIs, SLOs, SLAs, and error budgets
Understand SLIs (availability, latency, etc.) that measure reliability, SLOs (targets), SLAs (external contracts), error budgets that balance change velocity and reliability, and the cost of reliability (number of "nines").
- 3.2Service lifecycle, capacity planning, and incident mitigation
Understand service lifecycle management (plan/deploy/maintain/retire), capacity planning (quotas, limits, reservations, Dynamic Workload Scheduler), autoscaling (managed instance groups, Cloud Run, GKE), and mitigating incident impact on users (draining/redirecting traffic, adding capacity, rollback).
4Implementing observability practices and troubleshooting issues
- 4.1Instrumenting telemetry and managing logs
Understand collecting logs/metrics (Ops Agent, OpenTelemetry, Cloud Audit Logs, VPC Flow Logs, Google Cloud Managed Service for Prometheus), log optimization (filter/sampling/exclusions/cost), synthetic monitors, custom/log-based metrics, the Logs Explorer and query language, log export/retention (BigQuery/Pub/Sub/Cloud Storage), and redacting PII/PHI.
- 4.2Metrics, dashboards, alerts, and distributed tracing
Understand metric analysis via the Metrics Explorer, dashboards (PromQL, sharing, playbooks), alerting policies (SLI/SLO, cost) with third-party integration (e.g., PagerDuty), distributed tracing (OpenTelemetry, Cloud Trace, trace-log correlation), and troubleshooting infrastructure/CI-CD/app/performance/latency issues.
5Optimizing performance and cost
- 5.1Collecting performance information and Active Assist
Understand collecting performance information via application performance monitoring (APM) and obtaining continuous optimization hints through Active Assist insights/recommendations.
- 5.2FinOps and optimizing workload costs
Understand FinOps practices: Spot VMs, committed-use discounts (CUD) and sustained-use discounts (SUD), network tiers, optimizing resource utilization, observability costs, leveraging recommenders, and optimizing GKE/Cloud Run/Compute Engine workload costs.