Free Practice Questions for Google Professional Cloud DevOps Engineer Certification
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 22 Feb 2026
- Question bank last updated:
- 9 Oct 2026
Study with 361 exam-style practice questions designed to help you prepare for the Google Professional Cloud DevOps Engineer.
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Exam Details
Key information about Google Professional Cloud DevOps Engineer
- Multiple choice
Professional
Individuals who implement processes and capabilities throughout the systems development lifecycle using Google-recommended methodologies and tools. They enable efficient software and infrastructure delivery while balancing reliability with delivery speed, and optimize production systems for performance and cost.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Bootstrapping and maintaining a Google Cloud organization
1.1: Designing the overall resource hierarchy for an organization.
Considerations include:
- Organizing resources (e.g., application-centric, projects, folders) - Shared networking (e.g., Shared VPC, VPC Network Peering, Private Service Connect) - Multi-project monitoring and logging - Identity and Access Management (IAM) roles and organization-level policies - Creating and managing service accounts - Data residency
1.2: Managing infrastructure.
Considerations include:
- Infrastructure-as-code tooling and managed services (e.g., Infrastructure Manager, Cloud Foundation Toolkit, Config Connector, GitOps, Terraform, Helm) - Making infrastructure changes using Google-recommended practices and blueprints - Automation with scripting (e.g., Python, Go)
1.3: Designing a CI/CD architecture stack in Google Cloud, hybrid, and multi-cloud environments.
Considerations include:
- Continuous integration (CI) with Cloud Build - Continuous delivery (CD) with Cloud Deploy, including Kustomize and Skaffold - Artifact Registry configuration - Widely used third-party tooling (e.g., Git, Jenkins, Argo CD, Packer, kpt) - Security of CI/CD tooling
1.4: Managing multiple environments (e.g., staging, production).
Considerations include:
- Managing ephemeral environments - Managing configuration and policy - Managing Google Kubernetes Engine (GKE) clusters across an enterprise (e.g., fleets) - Safe and secure patching and upgrading practices
1.5: Enabling secure cloud development environments.
Considerations include:
- Configuring and managing cloud development environments (e.g., Cloud Workstations, Cloud Shell) - Bootstrapping environments with required tooling (e.g., custom images, IDE, Cloud SDK) - Leveraging AI to assist with development and operations (e.g., Gemini Code Assist, Gemini Cloud Assist, Gemini CLI)
2: Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads
2.1: Designing pipelines.
Considerations include:
- CI/CD of applications and infrastructure - Artifact management with Artifact Registry - Deployment to hybrid and multi-cloud environments (e.g., GKE) - CI/CD pipeline triggers - Configuring deployment processes (e.g., approval flows)
2.2: Implementing and managing pipelines.
Considerations include:
- Auditing and tracking deployments (e.g., Artifact Registry, Cloud Build, Cloud Deploy, Cloud Audit Logs) - Deployment strategies (e.g., canary, blue/green, rolling, traffic splitting, feature flags) and defining success metrics based on application or ML pipeline telemetry - Troubleshooting and mitigating deployment issues
2.3: Managing pipeline configuration and secrets.
Considerations include:
- Key management (e.g., Cloud Key Management Service) - Configuration and secret management (e.g., Secret Manager, Certificate Manager, Parameter Manager, Workload Identity Federation) - Build versus runtime secret injection
2.4: Securing the deployment pipeline.
Considerations include:
- Artifact Analysis and vulnerability scanning - Software supply chain security (e.g., Binary Authorization, Supply-chain Levels for Software Artifacts [SLSA] framework) - IAM policies based on environment
3: Applying site reliability engineering practices
3.1: Balancing change, velocity, and reliability of the service.
Considerations include:
- Defining SLIs (e.g., availability, latency), SLOs, and SLAs - Error budgets (e.g., Cloud Service Mesh definitions) - Opportunity cost of risk and reliability (e.g., number of “nines”)
3.2: Managing service lifecycle.
Considerations include:
- Service management (e.g., planning, deployment, maintenance, retirement) - Capacity planning (e.g., quotas, limits, reservations, Dynamic Workload Scheduler) - Autoscaling (e.g., managed instance groups, Cloud Run, GKE)
3.3: Mitigating incident impact on users.
Considerations include:
- Draining/redirecting traffic - Adding capacity - Rollback strategies
4: Implementing observability practices and troubleshooting issues
4.1: Instrumenting and collecting telemetry.
Considerations include:
- Collecting and importing logs (e.g., Ops Agent, OpenTelemetry, Cloud Audit Logs, VPC Flow Logs, Cloud Service Mesh) - Optimizing logs (e.g., filtering, sampling, exclusions, cost management, source considerations) - Collecting metrics (e.g., from applications, platforms, networking, Cloud Service Mesh, Google Cloud Managed Service for Prometheus, hybrid/multi-cloud environments) - Creating synthetic monitors to proactively probe application endpoints and workflows - Creating custom metrics, including log-based metrics
4.2: Managing and analyzing logs.
Considerations include:
- Analyzing logs using the Logs Explorer and the Logging query language - Exporting and retaining logs (e.g., routing to BigQuery, Pub/Sub, Cloud Storage) - Handling sensitive data (e.g., using log processors to redact personally identifiable information [PII], protected health information [PHI]) - Using Gemini Cloud Assist for AI-powered log analysis
4.3: Managing metrics, dashboards, and alerts.
Considerations include:
- Analyzing metrics using the Metrics Explorer - Managing dashboards (e.g., creating, filtering, sharing, playbooks, PromQL) - Configuring alerting and alerting policies (e.g., SLIs, SLOs, cost control) - Integrating with third-party alerting tools (e.g., webhooks, PagerDuty, Rootly) - Leveraging Gemini Cloud Assist for metrics interpretation
4.4: Capturing and analyzing distributed traces.
Considerations include:
- Utilizing tracing frameworks (e.g., OpenTelemetry) - Analyzing trace waterfalls and spans - Correlating trace IDs with structured logs - Employing Gemini Cloud Assist for trace analysis
4.5: Troubleshooting issues.
Considerations include:
- Infrastructure issues - CI/CD pipeline issues - Application issues - Observability issues - Performance and latency issues
5: Optimizing performance and cost
5.1: Collecting performance information in Google Cloud.
Considerations include:
- Application performance monitoring - Active Assist insights and recommendations
5.2: Implementing FinOps practices for optimizing resource utilization and costs.
Considerations include:
- Observability costs - Spot virtual machines (VMs) - Optimizing resource usage for cost and efficiency - Infrastructure cost planning (e.g., committed-use discounts, sustained-use discounts, network tiers) - Leveraging Google Cloud recommenders (e.g., cost, security, performance, manageability, reliability) - Optimizing individual workload costs (e.g., GKE, Cloud Run, Compute Engine)
Techniques & products