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The Google Professional Cloud DevOps Engineer certification demonstrates your ability to efficiently perform the development operations that can balance service reliability and delivery speed. After completing the qualifying exam, you will be proficient in using Google Cloud Platform to build software delivery pipelines, deploy & monitor services, and manage incidents. This expertise will give you access to endless opportunities for career development. Immediately after getting certified, you can start a career as a Cloud DevOps Engineer. Additionally, you can apply for the related job roles, such as a DevOps Infrastructure Engineer, a Cloud Solutions Architect, a GCP Cloud Native Architect, a Google Cloud Platform Data Architect, a GCP Cloud Data Engineer, and more. The average salary that the certificate holders can earn ranges between $137,000 and $180,000 per year.

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

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

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The Google Professional Cloud DevOps Engineer certification is designed to equip the specialists with the expertise required to perform the following technical tasks: applying the principles of site reliability engineering to services, optimizing service performance, implementing the strategies for service monitoring, building and implementing CI/CD pipelines for services, as well as managing service incidents. To get the certificate, the candidates need to complete a single exam covering a wide range of knowledge and skills.

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

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