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.
Reference: https://cloud.google.com/certification/cloud-devops-engineer
Concise contents
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A brief introduction to the course
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To be eligible for the Professional-Cloud-DevOps-Engineer certification exam, candidates need to have at least three years of experience in cloud computing, software development, and DevOps practices. They also need to have a good understanding of GCP services and tools, including Compute Engine, Kubernetes Engine, Cloud Storage, Cloud SQL, Cloud Build, and Cloud Monitoring. Candidates are recommended to take the GCP Associate Cloud Engineer and GCP Professional Cloud Architect certification exams before attempting the Professional-Cloud-DevOps-Engineer certification exam.
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Google Professional-Cloud-DevOps-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: CI/CD Pipeline Development | 26% | - 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. Managing secrets in CI/CD pipelines
- 2. Implementing shift-left testing
- 3. Configuring quality gates and code coverage
- Designing and implementing CI/CD pipelines
- 1. Setting up artifact management with Artifact Registry
- 2. Implementing build automation scripts
- 3. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
- 4. Configuring build caching strategies
- 5. Configuring build triggers and webhooks
|
| Topic 2: Microservices Architecture | 14% | - 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 Anthos Service Mesh / Istio
- 3. Implementing service-to-service authentication
- Container orchestration with GKE
- 1. Implementing workload deployment and scaling
- 2. Managing pod lifecycle and resource quotas
- 3. Designing Kubernetes cluster architectures
- 4. Configuring node pools and auto-scaling
|
| Topic 3: Monitoring, Logging, and Debugging | 12% | - Debugging and troubleshooting
- 1. Using Cloud Debugger and Error Reporting
- 2. Analyzing performance profiles with Cloud Profiler
- 3. Debugging with Cloud Trace distributed tracing
- Cloud Operations Suite
- 1. Configuring Cloud Monitoring metrics
- 2. Implementing Cloud Logging with log sinks
- 3. Creating uptime checks and alerts
- Log management
- 1. Implementing log-based metrics
- 2. Structuring logs for efficient querying
- 3. Configuring log retention policies
|
| Topic 4: Reliability and Site Reliability Engineering (SRE) | 24% | - Monitoring and observability
- 1. Configuring Cloud Monitoring and Logging
- 2. Creating dashboards for service health visibility
- 3. Setting up alerting policies and incident management
- 4. Implementing distributed tracing with Cloud Trace
- SLOs, SLIs, and SLAs
- 1. Setting appropriate Service Level Agreements
- 2. Defining and implementing Service Level Objectives
- 3. Error budget policies and management
- 4. Creating and interpreting Service Level Indicators
- Incident management
- 1. Configuring automated incident response
- 2. Implementing on-call procedures
- 3. Post-incident reviews and blameless postmortems
|
| Topic 5: Cloud Infrastructure Automation | 24% | - Deployment strategies
- 1. Blue-green deployments
- 2. Feature flags with Firebase Remote Config or LaunchDarkly
- 3. Canary releases and progressive rollouts
- 4. Rolling updates with Kubernetes
- Configuration management
- 1. Implementing configuration drift detection
- 2. Managing secrets with Secret Manager
- 3. Using Ansible for configuration management
- Infrastructure as Code (IaC)
- 1. Implementing with Terraform
- 2. Managing infrastructure modules and state
- 3. Implementing immutable infrastructure patterns
|