Prepare with Google Professional-Cloud-DevOps-Engineer exam practice material, pass for sure

Updated: Sep 02, 2026

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Google Professional-Cloud-DevOps-Engineer Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified - Professional Cloud DevOps Engineer Exam
Exam Number:Professional-Cloud-DevOps-Engineer
Passing Score:70%
Certificate Validity Period:2 years
Exam Duration:120 minutes
Available Languages:Portuguese, Spanish, English, Japanese
Exam Price:$200 USD
Real Exam Qty:50
Exam Format:Multiple Select, Multiple Choice
Related Certifications:Google Cloud Associate Cloud Engineer
Google Cloud Professional Cloud Developer
Google Cloud Professional Cloud Architect
Sample Questions:Google Professional-Cloud-DevOps-Engineer Sample Questions
Exam Way:Online proctored exam available through Pearson VUE or online proctoring. In-person testing at Kryterion test centers also available.
Pre Condition:Recommended: Associate Cloud Engineer certification and 3+ years industry experience including 1+ years Google Cloud experience. No mandatory prerequisites required.
Official Syllabus URL:https://cloud.google.com/certification/cloud-devops-engineer

Preparation Resources for Certification Test

Passing this Google exam is possible from the first attempt if you combine different preparation resources such as the ones offered by the vendor and the books available on Amazon. In case you’re on the lookout for the most effective revision materials, here’s our choice:

  • Google Cloud Professional Cloud DevOps Engineer Exam – All in One Guide: Get Certified Efficiently in Google Cloud! (Google Cloud Certification Series Book 7)

    This book is available on Amazon in Kindle format and has been published by Joseph Holbrook. Also, the candidates can buy it for almost $12. This guide follows all the topics tested in the certification exam and helps the applicants sharpen their skills in managing Google Cloud DevOps Engineer features. Besides, it helps entrants to fully prepare for the designation test as it includes more than 100 questions and detailed answers for each of them. Thus, the exam-takers will get used to the test structure and difficulty level. Plus, anyone who buys this guide will receive free access to additional learning resources that will consolidate their knowledge of Google Cloud apps and features.

  • Google Cloud DevOps Engineer Learning Path

    The vendor has developed a comprehensive learning path for candidates who want to pass the Professional Cloud DevOps Engineer exam. It includes several courses that will take the applicants through all the steps necessary to validate the necessary abilities to get the passing score from the first attempt. In particular, this learning kit includes the following training sessions:

    • The Google Cloud Fundamentals: Core Infrastructure class focuses on helping the candidates learn about storage and computing services which are included in Google Cloud Pack. Therefore, the exam-takers will learn how to use the BigQuery, App Engine, Cloud SQL and Storage, and Google Kubernetes Engine features.
    • The training dedicated to the development of a Google SRE culture helps the candidates gain advanced skills in providing production support and minimizing the gap between operations and development. During this course, applicants will learn the secrets of Site Reliability Engineering and how they can benefit from creating a Google SRE culture.
    • The Architecting with Google Cloud: Design and Process class helps the test-takers learn how to deploy Google Cloud features in a cost-effective and performant way.
    • The final training session included in this learning path is dedicated to monitoring, logging, and observability in Google Cloud. It teaches the candidates how to troubleshoot and improve Google Cloud infrastructure performance.

    Once the candidates go through the courses mentioned above, they will gain several skill badges that consolidate their skills in DevOps engineering. These badges are as follows:

    • The skill badge that validates the ability to use Google Cloud for performing foundational infrastructure tasks. It helps the candidates learn how to manage important application services such as Cloud Functions or Stackdriver.
    • The second badge is related to the implementation of DevOps features in Google Cloud. In particular, it will help the candidates improve their software delivery capacity with the help of Google Cloud and achieve a higher speed, availability, and stability.
    • The last badge helps the candidates understand how to use the Google Cloud Operations Suite for monitoring and logging. Thus, the applicants will learn how to use Google Cloud’s ins and outs for generating healthier and improved applications.

    Without doubt, a candidate who goes through this learning process provided by Google will have significantly higher chances to get certified in one go.

Audience for This Certification Exam

The target audience for the Google Professional Cloud DevOps Engineer test is formed of those individuals who want to learn how they can develop efficient operations and find a balance between the speed in delivering services and its reliability. Also, the candidates who are interested in taking this evaluation want to validate their skills in using the Google Cloud Platform at a professional level. Therefore, thanks to such an exam, applicants will learn how to develop pipelines related to software delivery, as well as become experts at monitoring and deploying services, together with managing and learning from them.

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

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

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

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