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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.

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For more info visit:

CCIE to Google Cloud DevOps Engineer Google cloud DevOps engineer practice exam Google Cloud documentation Google Cloud solutions

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

Career Prospects

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.

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

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

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