Try and practice the latest Google : Professional-Cloud-DevOps-Engineer real questions & answers

Last Updated: Aug 06, 2026

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Exam Details

The qualifying test for the Google Professional Cloud DevOps Engineer certification has the duration of 2 hours. The candidates will be asked to answer around 50 multiple-select and multiple-choice questions. The exam is available in the English language only. The students can take it either as an online proctored test or an onsite proctored exam at an authorized testing center. You can check the location of the nearest center in your region using the search on the official webpage. Each learner is required to pay the registration fee of $200 to schedule the exam.

If you don’t manage to pass the exam on the first try, you can retake it after 14 days. If you don't complete it for the second time, you will be required to wait for 60 days before you can sit for it for the third time. If you fail the third attempt, you will have to wait for 1 year before taking the test again.

Reference: https://cloud.google.com/certification/cloud-devops-engineer

Introduction to Google Professional Cloud DevOps Engineer Exam

Google Professional Cloud DevOps Engineer Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Professional Cloud DevOps engineer in the IT industry.

After passing this Professional Cloud DevOps Engineer Exam test, candidates get a certificate from Google that helps them to demonstrate their proficiency in Google Professional Cloud DevOps Engineer to their clients and employers.

Google Professional Cloud DevOps Engineer Certified Professional salary

The average salary of a Google Professional Cloud DevOps Engineer Certified Expert in different regions is as follows:

  • England: 130,500 POUND
  • Europe: 144,000 EURO
  • India: 25,00,000 INR
  • United State: 151,00 USD

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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Cloud Infrastructure Automation24%- Deployment strategies
  • 1. Rolling updates with Kubernetes
  • 2. Feature flags with Firebase Remote Config or LaunchDarkly
  • 3. Canary releases and progressive rollouts
  • 4. Blue-green deployments
- Infrastructure as Code (IaC)
  • 1. Implementing immutable infrastructure patterns
  • 2. Implementing with Terraform
  • 3. Managing infrastructure modules and state
- Configuration management
  • 1. Using Ansible for configuration management
  • 2. Managing secrets with Secret Manager
  • 3. Implementing configuration drift detection
Reliability and Site Reliability Engineering (SRE)24%- Incident management
  • 1. Implementing on-call procedures
  • 2. Configuring automated incident response
  • 3. Post-incident reviews and blameless postmortems
- Monitoring and observability
  • 1. Setting up alerting policies and incident management
  • 2. Configuring Cloud Monitoring and Logging
  • 3. Creating dashboards for service health visibility
  • 4. Implementing distributed tracing with Cloud Trace
- SLOs, SLIs, and SLAs
  • 1. Creating and interpreting Service Level Indicators
  • 2. Error budget policies and management
  • 3. Defining and implementing Service Level Objectives
  • 4. Setting appropriate Service Level Agreements
Monitoring, Logging, and Debugging12%- Log management
  • 1. Configuring log retention policies
  • 2. Structuring logs for efficient querying
  • 3. Implementing log-based metrics
- Cloud Operations Suite
  • 1. Creating uptime checks and alerts
  • 2. Configuring Cloud Monitoring metrics
  • 3. Implementing Cloud Logging with log sinks
- Debugging and troubleshooting
  • 1. Analyzing performance profiles with Cloud Profiler
  • 2. Using Cloud Debugger and Error Reporting
  • 3. Debugging with Cloud Trace distributed tracing
CI/CD Pipeline Development26%- Containerization and Docker
  • 1. Implementing container security best practices
  • 2. Managing container registries
  • 3. Creating optimized Docker images (multi-stage builds)
- Designing and implementing CI/CD pipelines
  • 1. Configuring build caching strategies
  • 2. Configuring build triggers and webhooks
  • 3. Setting up artifact management with Artifact Registry
  • 4. Implementing build automation scripts
  • 5. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
- CI/CD best practices
  • 1. Managing secrets in CI/CD pipelines
  • 2. Implementing shift-left testing
  • 3. Configuring quality gates and code coverage
Microservices Architecture14%- Container orchestration with GKE
  • 1. Configuring node pools and auto-scaling
  • 2. Designing Kubernetes cluster architectures
  • 3. Implementing workload deployment and scaling
  • 4. Managing pod lifecycle and resource quotas
- Service mesh and networking
  • 1. Implementing Anthos Service Mesh / Istio
  • 2. Implementing service-to-service authentication
  • 3. Configuring traffic management and load balancing
- API management
  • 1. Exposing APIs with Cloud Endpoints / Apigee
  • 2. Implementing API versioning strategies

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