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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

Domains Tested in Final Evaluation

The candidates who succeed in passing the Google Professional Cloud DevOps Engineer validation will add an international designation under their belt that helps them consolidate their position in the company and receive generous offers from their employers. So, if applicants want to differentiate themselves from other colleagues, they should demonstrate the following skills:

  • Identifying the most effective principles related to site reliability engineering and applying them to a definite service;
  • Identifying issues related to service performance and implementing solutions to optimize it;
  • Checking and implementing strategies related to service monitoring;
  • Coordinating teams to identify and manage incidents related to service development.
  • Implementing and building CI/CD service pipelines;

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

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

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