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Prerequisites

The Google Professional Cloud DevOps Engineer certification exam is designed for the Application Developers and Programmers who want to learn how to design, develop, and deploy Cloud applications. This test has no formal prerequisites. However, the individuals are recommended to have at least 3 years of industry experience, involving 1 or more years of experience with managing solutions on Google Cloud Platform.

The Google Professional Cloud DevOps Engineer exam is taken by the specialists who want to obtain international certification and get validated as professional engineers who can manage Cloud DevOps features.

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

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

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

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