Google Cloud Certified - Professional Cloud DevOps Engineer : Professional-Cloud-DevOps-Engineer

Professional-Cloud-DevOps-Engineer
  • Exam Code: Professional-Cloud-DevOps-Engineer
  • Exam Name: Google Cloud Certified - Professional Cloud DevOps Engineer Exam
  • Updated: Sep 16, 2026
  • Q & A: 208 Questions and Answers

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About Google Professional-Cloud-DevOps-Engineer Exam

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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:

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

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

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Exam Topics

To achieve good results in the Google Professional Cloud DevOps Engineer certification exam, the candidates need to develop a comprehensive understanding of its domains. The detailed outline of the topics and subtopics covered in the test is available on the official platform. Their brief overview is provided below:

  • Management of Service Incidents

    To answer the questions related to this part of the certification test, the students need to have the skills in coordinating roles and implementing the channels of communication in the course of the service incident. In addition, they need to have an understanding of how to perform the analysis of incident symptoms affecting the users. The test takers should also know how to mitigate the impact of an incident on the users. Finally, they need to have the sufficient competency in resolving issues (for example, Cloud Build, Jenkins, etc.) and documenting an issue in the postmortem.

  • Building & Implementation of CI/CD Pipelines for Services

    The second topic focuses on the ability of the examinees to manage various development environments (for instance, staging, production, etc.), manage infrastructure as code, as well as manage configuration & secrets. Moreover, the test takers are required to demonstrate their proficiency in designing & implementing CI/CD pipelines, deploying CI/CD tooling, and securing the deployment pipeline.

  • Application of Site Reliability Engineering Principles to Services

    This subject area measures the skills of the professionals in ensuring healthy collaboration for operations, including preventing burnout (for example, setting up automation procedures to prevent burnout) as well as fostering a learning culture & culture of blamelessness. Besides that, the candidates need to possess the expertise in managing the service life cycle as well as balancing the service change, velocity, and reliability.

  • Optimization of Service Performance

    Within this module, the learners need to show that they are capable of optimizing resource utilization, including determining resource costs, defining resource utilization levels, developing a plan to optimize areas of the lowest utilization/greatest cost, managing preemptible Virtual Machines, considering network pricing, and so on. They must also possess the skills in debugging application code as well as determining service performance issues.

  • Implementation of Strategies for Service Monitoring

    Here the applicants will be asked to show that they have the capacity to manage the Stackdriver Logging and Stackdriver Monitoring platforms. They should also know how to manage application logs as well as application metrics using Stackdriver Monitoring. Additionally, the individuals will require the competency in implementing logging & monitoring access control.

Google Professional Cloud DevOps Engineer Certification Path

The Google Professional Cloud DevOps Engineer Certification is the highest level of certification mainly focusing on the Solution Architect Professional. There is no prerequisite for this exam but still, it would be best to follow some sequence to prove immense knowledge as a Google Professional Cloud DevOps Engineer.

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

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

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