G O O G L E C E R T I F I C A T I O N
Professional Cloud DevOps Engineer Practice Exam
Exam Number: 1008 | Last updated April 21, 2026 | 1248+ questions across 5 vendor-aligned objectives
The Professional Cloud DevOps Engineer certification validates your ability to apply Google Cloud to real business problems. It is built for DevOps and site reliability engineers who own CI/CD pipelines, observability, and incident response on Google Cloud. A passing score proves you can map platform capabilities to outcomes and make defensible technical choices under time pressure.
Heavy-weighted areas define where study time pays back fastest: 25% targets Applying Site Reliability Engineering Principles to a Service (SLIs, SLOs, error budgets, toil reduction, postmortems); 22% targets Building and Implementing CI/CD Pipelines for a Service (Cloud Build, Cloud Deploy, progressive delivery, policy as code); 20% targets Applying Service Monitoring Strategies (Cloud Monitoring dashboards, uptime checks, alerting policies, Cloud Trace).
Supporting domains fill out the blueprint: 18% covers Implementing Service Monitoring Strategies (structured logging, log-based metrics, Cloud Profiler, error reporting); 15% covers Optimizing Service Performance (autoscaling tuning, latency budgets, capacity planning, chaos testing). Each still appears on the exam, so none can be safely skipped.
Every answer links to the source. Each explanation below includes a hyperlink to the exact Google documentation page the question was derived from. PowerKram is the only practice platform with source-verified explanations. Learn about our methodology →
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Question #1 - Applying Site Reliability Engineering Principles to a Service
A service has a 99.9% monthly availability SLO. Partway through the month it has experienced 45 minutes of downtime.
Which statement best reflects SRE error-budget thinking?
A) The service is fine because the SLO is only a guideline
B) The team has consumed most of its monthly error budget and should slow risky changes
C) The SLO should be raised to 99.99% to punish the team
D) Monitoring should be disabled to reset the budget
Show solution
Correct answers: B – Explanation:
A 99.9% monthly SLO allows about 43.2 minutes of downtime per 30-day month, so 45 minutes consumed essentially the whole budget. Raising the SLO does not help. Disabling monitoring hides reality. Treating the SLO as purely advisory defeats the purpose. Source: Check Source
Question #2 - Building and Implementing CI/CD Pipelines for a Service
An SRE needs to promote a containerized release through dev, staging, and prod GKE targets with approval gates between stages.
Which Google Cloud service is purpose-built for that delivery pipeline?
A) Cloud Scheduler
B) Cloud DNS
C) Cloud NAT
D) Cloud Deploy
Show solution
Correct answers: D – Explanation:
Cloud Deploy is a managed continuous delivery service for GKE and other runtimes, with environments and approval gates. Scheduler triggers jobs. Cloud DNS and Cloud NAT are networking. Source: Check Source
Question #3 - Applying Service Monitoring Strategies
A team wants to alert when an external API endpoint returns non-200 responses from multiple global regions for more than five minutes.
Which Google Cloud feature is designed for this?
A) Cloud Monitoring uptime checks with alerting policy
B) Cloud Build build triggers
C) Cloud Scheduler jobs that email on success
D) Artifact Registry scanning results
Show solution
Correct answers: A – Explanation:
Cloud Monitoring uptime checks probe endpoints from multiple regions and integrate with alerting policies. Cloud Build handles CI. Scheduler jobs do not evaluate external endpoints. Artifact Registry scans images. Source: Check Source
Question #4 - Implementing Service Monitoring Strategies
An SRE needs a numeric metric derived from a specific log pattern (e.g., HTTP 5xx count) so they can graph and alert on it.
Which Google Cloud capability does that?
A) Cloud Tasks queues
B) Cloud Memorystore for Redis
C) Log-based metrics in Cloud Logging
D) Cloud DNS policies
Show solution
Correct answers: C – Explanation:
Log-based metrics turn a log match into a numeric metric that Cloud Monitoring can chart and alert on. Cloud Tasks, Memorystore, and Cloud DNS are not log analytics features. Source: Check Source
Question #5 - Optimizing Service Performance
A service is exceeding its latency SLO during traffic spikes. CPU on instances goes to 95% before new pods start.
Which tuning is most likely to help?
A) Raise the HPA target CPU so pods scale later
B) Disable the HPA entirely and pin a small replica count
C) Remove all liveness probes so pods never restart
D) Lower the HPA target CPU so pods scale out sooner
Show solution
Correct answers: D – Explanation:
Lowering the HPA target CPU makes the autoscaler add pods earlier, reducing the latency tail during spikes. Raising the target does the opposite. Disabling HPA removes elasticity. Removing liveness probes masks failing pods. Source: Check Source
Question #6 - Building and Implementing CI/CD Pipelines for a Service
A team wants Cloud Build to run only when a pull request modifies files under the /services/billing/ path.
Which Cloud Build feature enables this?
A) Trigger with included/excluded file filters
B) Manually typing ‘go’ in the console
C) A Compute Engine cron running builds
D) A Pub/Sub subscription with random selection
Show solution
Correct answers: A – Explanation:
Cloud Build triggers support included/excluded file glob filters so builds run only when relevant files change. Manual execution is not automation. Compute Engine cron is not path-aware. Random selection is not deterministic. Source: Check Source
Question #7 - Applying Site Reliability Engineering Principles to a Service
After an outage caused by a deploy, a DevOps lead wants to ensure the team focuses on systemic fixes rather than individual blame.
Which SRE practice supports that goal?
A) A formal performance review of the on-call engineer
B) A blameless postmortem focused on causes and action items
C) Public naming of the engineer who deployed
D) Skipping the postmortem to save time
Show solution
Correct answers: B – Explanation:
Blameless postmortems capture root causes and actionable fixes without blaming individuals, which is core SRE practice. Performance reviews and public naming discourage honest reporting. Skipping the postmortem loses the learning. Source: Check Source
Question #8 - Applying Service Monitoring Strategies
An SRE wants a single view that shows SLI attainment, SLO target, and remaining error budget per service.
Which Google Cloud feature delivers that?
A) Cloud Storage bucket metadata
B) Cloud Asset Inventory export
C) Cloud Monitoring Service Monitoring (services and SLOs)
D) Cloud Build history
Show solution
Correct answers: C – Explanation:
Cloud Monitoring’s Services/SLO feature shows SLIs, SLOs, and error budgets per service. Storage metadata, asset inventory, and Build history do not track SLOs. Source: Check Source
Question #9 - Building and Implementing CI/CD Pipelines for a Service
A team wants to release new versions to 5% of users, watch metrics for 20 minutes, then promote if healthy.
Which release pattern describes this?
A) Canary (progressive) delivery
B) Big-bang release
C) Rollback-only releases
D) Manual zero-downtime cold cutover
Show solution
Correct answers: A – Explanation:
Canary delivery sends a small traffic slice to the new version and promotes on healthy signals. Big-bang releases swap all at once. Rollback-only is not a rollout. Cold cutover implies downtime. Source: Check Source
Question #10 - Optimizing Service Performance
A developer suspects a hot CPU path in a Go service is causing latency regressions but wants continuous, low-overhead profiling in production.
Which Google Cloud tool fits?
A) Cloud Source Repositories search
B) Cloud Profiler
C) Cloud DNS query logs
D) Cloud Build logs
Show solution
Correct answers: B – Explanation:
Cloud Profiler provides continuous, low-overhead production profiling across languages including Go. Source repo search, DNS logs, and Build logs are unrelated to runtime profiling. Source: Check Source
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Exam mode & learn mode · Score by objective · Updated April 21, 2026
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What the Cloud DevOps Engineer exam measures
- Applying Site Reliability Engineering Principles to a Service (25%): Apply Google Cloud practices to SLIs, SLOs, error budgets, toil reduction, postmortems.
- Building and Implementing CI/CD Pipelines for a Service (22%): Apply Google Cloud practices to Cloud Build, Cloud Deploy, progressive delivery, policy as code.
- Applying Service Monitoring Strategies (20%): Apply Google Cloud practices to Cloud Monitoring dashboards, uptime checks, alerting policies, Cloud Trace.
- Implementing Service Monitoring Strategies (18%): Apply Google Cloud practices to structured logging, log-based metrics, Cloud Profiler, error reporting.
- Optimizing Service Performance (15%): Apply Google Cloud practices to autoscaling tuning, latency budgets, capacity planning, chaos testing.
How to prepare for this exam
- Review the Professional Cloud DevOps Engineer official exam guide end to end before you commit a study plan, so every later hour is spent against the published blueprint.
- Complete the relevant Google Cloud Skills Boost learning path and treat its labs as non-optional rather than extra credit.
- Get hands-on practice in Qwiklabs sandbox, repeating the same tasks from memory until configuration feels routine.
- Apply what you learn in real-world project experience — your day job, a volunteer project, or an open-source contribution — so the concepts stick.
- Master one objective at a time, starting with the highest-weighted domain on the blueprint and moving down from there.
- Use PowerKram learn mode with feedback and sourced links to close gaps while the answer rationale is still fresh.
- Finish with PowerKram exam mode across all objectives under realistic time pressure before you book the real exam.
Career paths and salary outlook
Holding the Professional Cloud DevOps Engineer certification typically supports roles such as:
- Site Reliability Engineer: roughly $ 140,000 to $190,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Glassdoor.
- DevOps Engineer: roughly $ 120,000 to $170,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Levels.fyi.
- Platform Reliability Lead: roughly $ 155,000 to $210,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Payscale.
Official resources
Work directly from Google’s own preparation resources and treat third-party content as a supplement: