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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:

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

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

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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.

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

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