About the Exam

Google Cloud's Professional Cloud DevOps Engineer exam covers DevOps practices across the systems development lifecycle. It focuses on bootstrapping and maintaining a Google Cloud organization, applying site reliability engineering practices, building CI/CD pipelines with continuous testing, and implementing observability, troubleshooting, and performance and cost optimization. It is intended for experienced cloud professionals; Google recommends 3+ years of industry experience, including 1+ years designing and managing production systems using Google Cloud.

Exam Topics

  • Bootstrapping and maintaining a Google Cloud organization20%
  • Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads25%
  • Applying site reliability engineering practices18%
  • Implementing observability practices and troubleshooting issues25%
  • Optimizing performance and cost12%

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Last updated July 5, 2026 at 4:13 PM

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QuestionQ1

Optimizing performance and cost

You need to lower the cost of virtual machines (VMs) for your organization. After evaluating different options, you choose to use preemptible VM instances.

Which application is appropriate for preemptible VMs?

Explanation

Preemptible VMs may be stopped without notice, so they are appropriate for fault-tolerant batch workloads that can be retried. A GPU-based video-rendering platform can resume or rerun interrupted rendering work, while its input and output videos remain durably stored in the storage bucket.

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QuestionQ2

Applying site reliability engineering practices

Your company follows Site Reliability Engineering principles. You are preparing a postmortem for an incident caused by a software change that severely impacted users. You want to prevent a severe incident from occurring again in the future. What should you do?

Explanation

SRE postmortems should result in blameless, concrete preventative action items that improve systems and processes. Requiring successful pre-release execution of test cases that detect the relevant error type directly reduces the likelihood that the same software-change failure reaches users. Google SRE guidance recommends preventative fixes and emphasizes that changing automated systems and processes is more reliable than trying to change human behavior.

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QuestionQ3

Implementing observability practices and troubleshooting issues

You are designing a hosting architecture in Google Kubernetes Engine (GKE) for business-critical applications. The applications expose custom metrics for monitoring with Prometheus. You need to collect the application metrics for alerting and troubleshooting. You want to minimize manual effort and ongoing maintenance while following Google-recommended practices. What should you do?

Explanation

Google Cloud Managed Service for Prometheus managed collection is Google’s recommended approach for Kubernetes environments. It collects Prometheus metrics from GKE workloads while the managed Kubernetes operator handles collector deployment, scrape configuration, scaling, and related operational maintenance; the collected data supports Cloud Monitoring querying and alerting.

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QuestionQ4

Optimizing performance and cost

Your company runs services using Google Kubernetes Engine (GKE). The GKE clusters in the development environment run applications with verbose logging enabled. Developers inspect logs by using the kubectl logs command and do not use Cloud Logging. Applications do not have a defined uniform logging structure.

You need to minimize the costs associated with application logging while continuing to collect GKE operational logs. What should you do?

Explanation

GKE SYSTEM logging collects system logs required for cluster operations, while WORKLOAD logging collects logs generated by non-system application containers. Configuring the development cluster with --logging=SYSTEM prevents verbose application workload logs from being ingested into Cloud Logging while preserving GKE operational/system logs. About GKE logs

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QuestionQ5

Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads

You are configuring a CI/CD pipeline natively in Google Cloud. You want builds in a pre-production Google Kubernetes Engine (GKE) environment to be automatically load-tested before promotion to the production GKE environment. You must ensure that only builds that pass this test are deployed to production. You want to follow Google-recommended practices. How should you configure this pipeline with Binary Authorization?

Explanation

Binary Authorization can require a trusted attestation before an image is deployed to production. An automated load-test workload should create the attestation only after the test succeeds, using an asymmetric signing key held in Cloud KMS. Workload Identity Federation for GKE is the recommended way for a GKE workload to obtain the IAM authorization needed to use Google Cloud APIs, avoiding less-secure, long-lived service account JSON keys stored as Kubernetes Secrets.

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