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Last Updated: Aug 15, 2026

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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

Who should take the Google Professional Cloud DevOps Engineer exam

Individuals should pursue the Professional Cloud DevOps Engineer practice exam if they want to demonstrate their expertise and ability to design plan, and prototype a GCP DevOps, implement a GCP Virtual Private Cloud (VPC), implement DevOps security. It's perfect for DevOps engineers, systems administrators or operations team members, or simply any professional who wants in on this specific area of IT and cloud.

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

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What is the duration, language, and format of Google Professional Cloud DevOps Engineer Exam

  • Format: Multiple choices, multiple select
  • Passing score: 80%
  • Number of Questions: 50-60
  • Language: English
  • Length of Examination: 120 minutes

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

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