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Audience for This Certification Exam
The target audience for the Google Professional Cloud DevOps Engineer test is formed of those individuals who want to learn how they can develop efficient operations and find a balance between the speed in delivering services and its reliability. Also, the candidates who are interested in taking this evaluation want to validate their skills in using the Google Cloud Platform at a professional level. Therefore, thanks to such an exam, applicants will learn how to develop pipelines related to software delivery, as well as become experts at monitoring and deploying services, together with managing and learning from them.
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
Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Microservices Architecture | 14% | - API management
- 1. Exposing APIs with Cloud Endpoints / Apigee
- 2. Implementing API versioning strategies
- Service mesh and networking
- 1. Implementing Anthos Service Mesh / Istio
- 2. Implementing service-to-service authentication
- 3. Configuring traffic management and load balancing
- Container orchestration with GKE
- 1. Designing Kubernetes cluster architectures
- 2. Implementing workload deployment and scaling
- 3. Configuring node pools and auto-scaling
- 4. Managing pod lifecycle and resource quotas
|
| Cloud Infrastructure Automation | 24% | - Deployment strategies
- 1. Feature flags with Firebase Remote Config or LaunchDarkly
- 2. Rolling updates with Kubernetes
- 3. Blue-green deployments
- 4. Canary releases and progressive rollouts
- Infrastructure as Code (IaC)
- 1. Implementing with Terraform
- 2. Implementing immutable infrastructure patterns
- 3. Managing infrastructure modules and state
- Configuration management
- 1. Implementing configuration drift detection
- 2. Using Ansible for configuration management
- 3. Managing secrets with Secret Manager
|
| Monitoring, Logging, and Debugging | 12% | - 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. Analyzing performance profiles with Cloud Profiler
- 2. Debugging with Cloud Trace distributed tracing
- 3. Using Cloud Debugger and Error Reporting
- Log management
- 1. Configuring log retention policies
- 2. Implementing log-based metrics
- 3. Structuring logs for efficient querying
|
| CI/CD Pipeline Development | 26% | - Containerization and Docker
- 1. Managing container registries
- 2. Implementing container security best practices
- 3. Creating optimized Docker images (multi-stage builds)
- 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. Configuring build triggers and webhooks
- 2. Implementing build automation scripts
- 3. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
- 4. Configuring build caching strategies
- 5. Setting up artifact management with Artifact Registry
|
| Reliability and Site Reliability Engineering (SRE) | 24% | - Incident management
- 1. Implementing on-call procedures
- 2. Configuring automated incident response
- 3. Post-incident reviews and blameless postmortems
- 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
- SLOs, SLIs, and SLAs
- 1. Error budget policies and management
- 2. Setting appropriate Service Level Agreements
- 3. Creating and interpreting Service Level Indicators
- 4. Defining and implementing Service Level Objectives
|
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