Google Professional-Cloud-DevOps-Engineer Q&A - in .pdf

  • Professional-Cloud-DevOps-Engineer pdf
  • Exam Code: Professional-Cloud-DevOps-Engineer
  • Exam Name: Google Cloud Certified - Professional Cloud DevOps Engineer Exam
  • Updated: Oct 27, 2026
  • Q & A: 208 Questions and Answers
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  • Exam Code: Professional-Cloud-DevOps-Engineer
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  • Q & A: 208 Questions and Answers
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  • Exam Code: Professional-Cloud-DevOps-Engineer
  • Exam Name: Google Cloud Certified - Professional Cloud DevOps Engineer Exam
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  • Q & A: 208 Questions and Answers
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Professional-Cloud-DevOps-Engineer exam dumps

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.

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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:

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

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.

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

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

SectionWeightObjectives
Topic 1: Applying site reliability engineering practices18%- Manage incidents and outages
  • 1. Perform postmortems and root cause analysis
  • 2. Implement improvements to prevent recurrence
  • 3. Detect and respond to issues quickly
- Design for resilience
  • 1. Plan redundancy and fault tolerance
  • 2. Test failure scenarios and recovery
  • 3. Automate remediation and self-healing
- Define reliability goals
  • 1. Use error budgets to balance change and stability
  • 2. Establish SLIs, SLOs, SLAs
  • 3. Align reliability with business needs
Topic 2: Building and implementing CI/CD pipelines25%- Implement and secure pipelines
  • 1. Manage pipeline secrets and credentials
  • 2. Enforce security scanning and compliance checks
  • 3. Automate build, test, and deployment stages
- Optimize and maintain pipelines
  • 1. Monitor pipeline performance
  • 2. Improve speed and reliability
  • 3. Handle rollbacks and versioning
- Design CI/CD pipelines
  • 1. Support hybrid/multi-cloud deployment
  • 2. Plan pipelines for apps, infrastructure, ML workloads
  • 3. Integrate artifact management
Topic 3: Bootstrapping and maintaining a Google Cloud organization20%- Set up governance and compliance
  • 1. Manage resource quotas and limits
  • 2. Configure audit logging and monitoring
  • 3. Implement organization policies
- Manage configuration and deployment
  • 1. Automate resource provisioning
  • 2. Use Infrastructure as Code tools
  • 3. Enforce consistency across environments
- Design resource hierarchy
  • 1. Organize resources by project/folder/application
  • 2. Define IAM policies and access control
  • 3. Plan shared networking and connectivity
Topic 4: Optimizing performance and cost17%- Apply FinOps practices
  • 1. Right-size resources and eliminate waste
  • 2. Monitor and analyze cloud costs
  • 3. Use spot instances and committed use discounts
- Improve system performance
  • 1. Use caching and content delivery
  • 2. Optimize resource allocation
  • 3. Tune applications and infrastructure
- Balance cost, performance, and reliability
  • 1. Optimize over time
  • 2. Make tradeoff decisions based on data
  • 3. Plan capacity and scaling
Topic 5: Implementing observability and troubleshooting20%- Build monitoring and logging systems
  • 1. Use Cloud Monitoring, Cloud Logging, Cloud Trace
  • 2. Collect and analyze metrics, logs, traces
  • 3. Create dashboards and alerts
- Optimize observability strategy
  • 1. Reduce noise and prioritize signals
  • 2. Integrate observability into development
  • 3. Scale monitoring for large environments
- Troubleshoot complex issues
  • 1. Analyze performance and latency
  • 2. Isolate problems across layers
  • 3. Trace requests end-to-end

Google Cloud Certified - Professional Cloud DevOps Engineer Sample Questions:

Question #1

You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_datasetYou need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

  • A. Create an aggregated Iog sink in the Dev and Prod folders
  • B. Create a log sink in each project
  • C. Create a single aggregated log sink at the organization level.
  • D. Create two aggregated log sinks at the organization level, and filter by project ID
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Question #2

You have an application running in Google Kubernetes Engine. The application invokes multiple services per request but responds too slowly. You need to identify which downstream service or services are causing the delay. What should you do?

  • A. Use a distributed tracing framework such as OpenTelemetry or Stackdriver Trace.
  • B. Investigate the Liveness and Readiness probes for each service.
  • C. Analyze VPC flow logs along the path of the request.
  • D. Create a Dataflow pipeline to analyze service metrics in real time.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

Your application images are built and pushed to Google Container Registry (GCR). You want to build an automated pipeline that deploys the application when the image is updated while minimizing the development effort. What should you do?

  • A. Use a custom builder in Cloud Build to trigger a Jenkins pipeline.
  • B. Use Cloud Build to trigger a Spinnaker pipeline.
  • C. Use Cloud Pub/Sub to trigger a custom deployment service running in Google Kubernetes Engine (GKE).
  • D. Use Cloud Pub/Sub to trigger a Spinnaker pipeline.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Question #4

Your team deploys applications to three Google Kubernetes Engine (GKE) environments development staging and production You use GitHub reposrtones as your source of truth You need to ensure that the three environments are consistent You want to follow Google-recommended practices to enforce and install network policies and a logging DaemonSet on all the GKE clusters in those environments What should you do?

  • A. Use Google Cloud Deploy to deploy the DaemonSet and use Policy Controller to configure the network policies Use Cloud Monitoring to detect drifts from the source in the repository and Cloud Functions tocorrect the drifts
  • B. Use Cloud Build to render and deploy the network policies and the DaemonSet Set up a Policy Controller to enforce the configurations for the three environments
  • C. Use Google Cloud Deploy to deploy the network policies and the DaemonSet Use Cloud Monitoring to trigger an alert if the network policies and DaemonSet drift from your source in the repository.
  • D. Use Cloud Build to render and deploy the network policies and the DaemonSet Set up Config Sync to sync the configurations for the three environments
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

Question #5

You deploy a new release of an internal application during a weekend maintenance window when there is minimal user traffic. After the window ends, you learn that one of the new features isn't working as expected in the production environment. After an extended outage, you roll back the new release and deploy a fix. You want to modify your release process to reduce the mean time to recovery so you can avoid extended outages in the future. What should you do?
Choose 2 answers

  • A. Configure a CI server.Add a suite of unit tests to your code and have your CI server run them on commit and verify any changes.
  • B. Integrate a code linting tool to validate coding standards before any code is accepted into the repository.
  • C. Adopt the blue/green deployment strategy when releasing new code via a CD server.
  • D. Before merging new code, require 2 different peers to review the code changes.
  • E. Require developers to run automated integration tests on their local development environments before release.
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

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