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Exam Information
Official specifications published by Google
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Skills this practice test covers
- Design a resource hierarchy of folders and projects with shared networking and organization policies.
- Release apps and infrastructure through Cloud Build, Artifact Registry and Cloud Deploy pipelines.
- Choose a canary, blue/green or rolling release, and plan how to roll it back.
- Set SLIs, SLOs and error budgets, and use them to decide when releases should slow down.
- Filter, export and retain logs, then build log-based metrics, dashboards and alerting policies.
- Trace slow requests with Cloud Trace and Error Reporting, and cut spend with Spot VMs and recommenders.
Explore related topics
PCDOE exam content
5 domains · 34 topics · 170 of 180 questions sorted by domain
Bootstrapping and maintaining a Google Cloud organization35 questions
This domain covers the groundwork under every pipeline: the resource hierarchy, IAM roles and organization policies, infrastructure as code, and the environments teams work in. It also asks how to run GKE clusters across an enterprise and patch them safely.
Skills tested
- Lay out folders and projects with shared networking and logging that spans many projects.
- Create service accounts and grant IAM roles under organization-level policies.
- Change infrastructure through code with Terraform, Helm, Config Connector or the Cloud Foundation Toolkit.
- Decide how many environments to keep, and when a short-lived ephemeral one is the better choice.
- Prepare Cloud Workstations with custom images, the Cloud SDK and Gemini Code Assist.
Questions by topic
- Managing infrastructure7
- Creating and managing service accounts4
- Designing the overall resource hierarchy for an organization4
- Automation with scripting4
- Managing Google Kubernetes Engine (GKE) clusters across an enterprise4
- Identity and Access Management (IAM) roles and organization-level policies4
- Enabling secure cloud development environments4
- Leveraging AI to assist with development and operations4
Building and implementing CI/CD pipelines for applications and infrastructure51 questions
This domain is about the pipeline itself. Candidates design builds and releases with Cloud Build, Cloud Deploy and Artifact Registry, handle keys and secrets, and protect the software supply chain from the first build to production.
Skills tested
- Configure pipeline triggers, test each new version, and add an approval flow before production.
- Pick a canary, blue/green, rolling or traffic-splitting release, and fix a deployment that fails.
- Keep keys in Cloud Key Management Service and secrets in Secret Manager, injected at build time or at runtime.
- Scan images in Artifact Registry and allow only attested images to deploy with Binary Authorization.
- Apply the same pipeline practices to GKE clusters, managed instance groups and serverless apps.
Questions by topic
- Securing the CI/CD deployment pipeline5
- Applying CI/CD practices to infrastructure5
- Designing and managing CI/CD pipelines8
- CI/CD pipeline triggers4
- Managing CI/CD configuration and secrets4
- Implementing CI/CD pipelines3
- Software supply chain security5
- Testing a new application version in the pipeline4
- Deployment to hybrid and multi-cloud environments5
- Deployment strategies4
- Troubleshooting and mitigating deployment issues4
Applying site reliability engineering practices to applications30 questions
This domain turns reliability into numbers a team can act on. Candidates define SLIs, set SLOs and SLAs, spend error budgets and plan capacity, then limit the harm of an incident by draining traffic, adding capacity or rolling back.
Skills tested
- Define SLIs such as availability and latency, and set SLOs the business can accept.
- Use an error budget to weigh new features against reliability work.
- Plan a new service with an onboarding checklist, a launch plan, quotas and limits.
- Configure autoscaling for managed instance groups, Cloud Run and GKE.
- Reduce user impact during an incident by redirecting traffic, adding capacity or rolling back.
Questions by topic
- Balancing change, velocity, and reliability of the service18
- Mitigating incident impact on users8
- Capacity planning4
Implementing observability practices29 questions
Observability is how a team sees what its systems are doing. This domain covers collecting, filtering, exporting and keeping logs, turning logs into metrics, and building dashboards and alerting policies, while protecting sensitive data such as personally identifiable information.
Skills tested
- Collect logs with the Cloud Logging agent, Cloud Audit Logs and VPC Flow Logs.
- Lower logging cost with filters, sampling and exclusions, and export logs to BigQuery or Pub/Sub.
- Create custom metrics from logs and study them in Metrics Explorer.
- Set up synthetic monitors and alerting policies built on SLOs.
Questions by topic
- Managing logs7
- Logging optimization4
- Creating custom metrics from logs4
- Creating synthetic monitors5
- Managing metrics4
- Managing dashboards and alerts4
- Implementing debugging tools in Google Cloud1
Optimizing performance and troubleshooting25 questions
The last domain asks what is wrong and what it costs. Candidates troubleshoot infrastructure, application, pipeline and observability problems, instrument code for Cloud Trace and Error Reporting, and lower spend with Spot VMs and Google Cloud recommenders.
Skills tested
- Find the source of latency with application instrumentation and Cloud Trace.
- Group and read application errors in Error Reporting.
- Diagnose a failing pipeline, a missing metric or an infrastructure fault.
- Cut costs with Spot VMs, cheaper observability settings and Google Cloud recommenders.
Questions by topic
- Troubleshooting issues4
- Google Cloud recommenders4
- Implementing debugging tools in Google Cloud8
- Performance and latency issues5
- Spot virtual machines (VMs)4
Requirements
- No formal prerequisites: anyone can book the exam.
- Recommended: three or more years of industry experience, including a year or more designing and managing production systems on Google Cloud.
- Recommended: practical experience with DevOps tools, CI/CD pipelines and site reliability engineering principles.
About the PCDOE exam
Google Cloud's PCDOE exam is for engineers who ship software through automated pipelines and keep the running services reliable. It covers five areas: setting up a Google Cloud organization, continuous integration and delivery (CI/CD) pipelines, site reliability engineering (SRE), observability, and performance and cost. Google recommends three or more years of industry experience, with at least one year designing and managing production systems on Google Cloud. Passing earns the Google Cloud Professional Cloud DevOps Engineer certification.
The PlanetCert practice test gives the most questions to CI/CD pipelines, from build triggers and Artifact Registry to Binary Authorization and release strategies. Each explanation says why one option fits the case, such as build-time against runtime secret injection, or an SLO against an SLA. Study Mode suits the SRE questions, where the answer often depends on how many "nines" a service promises. Brainy AI Assistant can work through an error budget step by step, or explain when a log export belongs in BigQuery and when in Pub/Sub.
How to earn the certification
- Earns
- Google Cloud Professional Cloud DevOps EngineerProfessional
The certification is valid for two years.
- Before
- GCP-ACE Google Cloud Associate Cloud Engineer
Optional, not a formal prerequisite. It is Google Cloud's associate-level engineer exam, one level below this Professional exam.
- GCP-ACE Google Cloud Associate Cloud Engineer
How to prepare for PCDOE
- 1
Mark up Google's exam guide
Google's guide lists considerations under each section, such as App Hub, Kustomize and Skaffold. Mark every item not yet used by hand, and give those items the first lab hours.
- 2
Ship one app through a pipeline
Trigger a Cloud Build job from a Git push, store the image in Artifact Registry, and promote it with Cloud Deploy from staging to production. Add an approval step and a canary release on the way.
- 3
Write SLOs for a sample service
Choose SLIs for availability and latency, set an SLO, and work out the error budget it leaves. Then decide what the team should do when half the budget is gone early in the month.
- 4
Route logs, then tune alerts
Export logs to BigQuery and Pub/Sub, add exclusions to cut cost, and create a log-based metric. Attach an alerting policy to that metric and check that the alert fires.
- 5
Practise domain by domain, then timed
Work through the PCDOE practice test in Study Mode one domain at a time, starting with CI/CD pipelines. Finish with a Timed Mode run, and ask Brainy AI Assistant about each missed release-strategy question.
Start a timed PCDOErun
Google Cloud Skills Boost has a DevOps Engineer, SRE Learning Path for this certification, with hands-on labs and skill badges.
Who PCDOE is for
- Platform and DevOps engineers who own build and release pipelines on Google Cloud.
- Site reliability engineers who set SLOs and lead incident response for production services.
- Cloud architects who also run deployments and operations on Google Cloud.
- Systems administrators who are taking on automation and monitoring work in Google Cloud.
Typical job titles: DevOps engineer · Site reliability engineer · Platform engineer · Cloud infrastructure engineer · DevOps architect
PCDOE glossary
18 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- App Hub
- A Google Cloud tool that organizes resources around the applications they belong to, not only by project.
- Artifact Registry
- Google Cloud's store for build outputs such as container images and packages, which can also scan them for vulnerabilities.
- Binary Authorization
- A deploy-time control that lets a container image run only when it carries the attestations a policy requires.
- Blue/green
- A release strategy that runs the old and new versions side by side and moves all traffic at once, so rollback is a quick switch back.
- Canary
- A release strategy that sends a small share of traffic to the new version first and widens the rollout only if it behaves well.
- Cloud Build
- Google Cloud's managed service that runs build and test steps, often as the continuous integration stage of a pipeline.
- Cloud Deploy
- Google Cloud's managed continuous delivery service, which promotes a release through a sequence of target environments.
- Cloud Trace
- A distributed tracing tool that shows how long each step of a request takes as it passes between services.
- Config Connector
- A Kubernetes add-on that lets teams create and manage Google Cloud resources as Kubernetes objects.
- Ephemeral environments
- Short-lived environments, created for one test or one branch and deleted when that work is done.
- Error budgets
- The amount of unreliability an SLO allows over a period. When the budget is spent, the team slows releases and fixes reliability first.
- Error Reporting
- A Google Cloud service that groups application errors and crashes, so a team can see how often each one happens.
- Metrics Explorer
- The Cloud Monitoring tool for building quick, one-off charts of any metric while investigating a problem.
- Secret Manager
- Google Cloud's service for storing passwords, API keys and certificates, with access controlled through IAM.
- SLIs
- Service level indicators: measured signals of service health, such as the share of requests that succeed or how fast they return.
- SLOs
- Service level objectives: the target an SLI must meet over a period, such as a set share of successful requests in a month.
- Spot virtual machines (VMs)
- Low-cost Compute Engine VMs that Google Cloud can stop at short notice, suited to work that can be interrupted.
- Supply-chain Levels for Software Artifacts (SLSA)
- A framework of security levels for how software is built, so a team can show that an artifact came from a trusted process.