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Syllabus

PCDOE syllabus

5 domains and 85 topics, updated November 2026

Overview Syllabus Notes Patterns Revision Sample questions Practise

The Professional Cloud DevOps Engineer exam tests SRE practice and delivery automation on Google Cloud: SLOs and error budgets, Cloud Build and Cloud Deploy pipelines, supply chain security, observability, incident handling and cost. The guide has five sections (foundations 18%, CI/CD 30%, SRE 18%, observability 22%, performance and cost 12%), effective 23 November 2026. This outline maps them onto the five study domains: organization and infrastructure foundations sit with SRE principles, and environments and developer tooling sit with CI/CD. Tick topics off as you cover them. Your ticks are saved in this browser.

This outline follows the official PCDOE exam guide (PDF). Check it for the exact wording.

Applying SRE principles

SLIs, SLOs, error budgets, the service lifecycle, capacity and autoscaling (guide section 3), plus the organization and infrastructure foundations of section 1.1 and 1.2.

Balancing change, velocity and reliability (3.1)

SLIs such as availability and latency as good events over valid events, SLOs as internal targets, SLAs as contracts with consequences
Error budgets, error budget policies, and how Cloud Monitoring and Cloud Service Mesh define them
Burn rate and multi-window alerting on SLOs
The opportunity cost of each extra nine, and what the nines mean in downtime

Managing the service lifecycle (3.2)

Planning, deployment, maintenance and retirement of a service, including production readiness reviews
Capacity planning with quotas, limits, reservations and Dynamic Workload Scheduler
Autoscaling managed instance groups, Cloud Run and GKE
Feedback loops, toil reduction and DORA metrics for continuous improvement

Mitigating incident impact (3.3, see also incident management)

Draining and redirecting traffic, adding capacity and rolling back

Resource hierarchy and governance (1.1)

Organizing resources with organization, folders and projects, application-centric layouts
Shared VPC, VPC Network Peering, Network Connectivity Center and Private Service Connect
Multi-project monitoring with metrics scopes and aggregated log sinks
IAM roles, organization policies, and creating and managing service accounts safely
Data residency through resource location constraints, Assured Workloads and regional storage

Managing infrastructure (1.2)

Infrastructure as code with Terraform, Infrastructure Manager, Application Design Center, Config Connector, GitOps and Helm
Google-recommended practices and blueprints, Terraform modules
Automating operations with scripts in Python and Go
Using Provisioned Throughput so agents stay reliable

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Building and implementing CI/CD pipelines

Cloud Build, Cloud Deploy, Artifact Registry, deployment strategies, secrets, supply chain security (section 2), plus the CI/CD stack, multiple environments and development environments from 1.3 to 1.5. The largest domain.

CI/CD architecture stack (1.3)

Continuous integration with Cloud Build
Continuous delivery with Cloud Deploy, using Kustomize and Skaffold
Artifact Registry repository configuration
Third-party tools: Git, Argo CD, Flux, kpt, Jenkins and Packer
Securing the CI/CD tooling itself

Designing and building pipelines (2.1)

Pipelines for applications and infrastructure
Artifact management with Artifact Registry
Deploying to hybrid and multicloud environments such as GKE
Pipeline triggers
Deployment processes with approval flows

Implementing and managing pipelines (2.2)

Auditing and tracking deployments with Artifact Registry, Cloud Build, Cloud Deploy and Cloud Audit Logs
Canary, blue/green, rolling, traffic splitting and feature flags
Success metrics from application or ML pipeline telemetry
Continuous testing and CI/CD for ML workloads
Troubleshooting and mitigating deployment issues

Pipeline configuration and secrets (2.3)

Cloud KMS key management
Secret Manager, Certificate Manager, Parameter Manager and Workload Identity Federation
Build-time versus runtime secret injection
Managing short-lived credentials

Securing the pipeline (2.4)

Artifact Analysis and vulnerability scanning
Binary Authorization and the SLSA framework
IAM policies that differ per environment

Multiple runtime environments (1.4)

Ephemeral environments
Configuration and policy management across environments
GKE fleets and managing clusters across an enterprise
Managing serverless environments
Safe and secure patching and upgrading
Isolating agent execution environments with GKE Agent Sandbox

Cloud development environments (1.5)

Cloud Workstations and Cloud Shell
Bootstrapping environments with custom images, IDEs and the Google Cloud SDK
AI assistance with Gemini Code Assist, Gemini Cloud Assist, Gemini CLI and Antigravity
Configuring and using MCP servers

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Implementing observability

Collecting, routing, analysing and alerting on logs, metrics and traces (guide 4.1 to 4.3 and trace analysis).

Instrumenting and collecting telemetry (4.1)

Collecting logs with the Ops Agent, OpenTelemetry, Cloud Audit Logs, VPC Flow Logs and Cloud Service Mesh
Optimizing logs with filters, sampling, exclusions and cost controls
Collecting metrics from applications, platforms, networking, Cloud Service Mesh, Managed Service for Prometheus and hybrid environments
Synthetic monitors and uptime checks
Custom and log-based metrics
Emitting logs and metrics from GKE, Cloud Run, agents and the Agent Development Kit

Managing and analyzing logs (4.2)

Logs Explorer and the Logging query language, with AI-assisted analysis
Exporting and retaining logs with sinks to BigQuery, Pub/Sub and Cloud Storage
Redacting PII and PHI
Capturing and analysing traces with OpenTelemetry, waterfalls, spans and trace-to-log correlation

Metrics, dashboards and alerts (4.3)

Metrics Explorer and PromQL
Creating, filtering and sharing dashboards, and linking playbooks
Alerting policies based on SLIs and SLOs, with cost control
Third-party alerting with webhooks, PagerDuty, Slack and Discord
AI-assisted metrics interpretation

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Managing incidents

Mitigating user impact and troubleshooting (guide 3.3 and 4.4), plus the incident response process and postmortems that the SRE material assumes.

Mitigating incident impact (3.3)

Draining or redirecting traffic with load balancers, DNS and Cloud Run traffic splits
Adding capacity and the traps around quotas and downstream load
Rollback strategies for Cloud Run, Cloud Deploy, GKE, instance groups and feature flags

Troubleshooting (4.4)

Infrastructure issues: VMs, networks, quotas, GKE Pod states, Cloud Run
CI/CD pipeline issues in Cloud Build, Cloud Deploy and Binary Authorization
Application issues with Error Reporting, logs, metrics and traces
Observability issues: missing logs, missing metrics, alerts that do not fire
Performance and latency issues

Incident response practice

Incident commander, operations, communications and planning roles
Severity, declaring early, communication cadence and handoffs
Blameless postmortems and tracked action items

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Optimizing performance and cost

Performance data, Active Assist and FinOps (guide section 5).

Collecting performance information (5.1)

Application and agent performance monitoring with Trace, Profiler and Application Monitoring
Active Assist insights and recommendations

FinOps practices (5.2)

Observability costs for logs, metrics and traces
Spot VMs
Optimizing resource usage with rightsizing and scheduling
Infrastructure cost planning with committed use discounts, sustained use discounts and network tiers
Cost, security, performance, manageability and reliability recommenders
Optimizing GKE, Cloud Run and Compute Engine workloads
Provisioned Throughput and token consumption for AI workloads

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