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Exam Topics

To achieve good results in the Google Professional Cloud DevOps Engineer certification exam, the candidates need to develop a comprehensive understanding of its domains. The detailed outline of the topics and subtopics covered in the test is available on the official platform. Their brief overview is provided below:

  • Management of Service Incidents

    To answer the questions related to this part of the certification test, the students need to have the skills in coordinating roles and implementing the channels of communication in the course of the service incident. In addition, they need to have an understanding of how to perform the analysis of incident symptoms affecting the users. The test takers should also know how to mitigate the impact of an incident on the users. Finally, they need to have the sufficient competency in resolving issues (for example, Cloud Build, Jenkins, etc.) and documenting an issue in the postmortem.

  • Application of Site Reliability Engineering Principles to Services

    This subject area measures the skills of the professionals in ensuring healthy collaboration for operations, including preventing burnout (for example, setting up automation procedures to prevent burnout) as well as fostering a learning culture & culture of blamelessness. Besides that, the candidates need to possess the expertise in managing the service life cycle as well as balancing the service change, velocity, and reliability.

  • Building & Implementation of CI/CD Pipelines for Services

    The second topic focuses on the ability of the examinees to manage various development environments (for instance, staging, production, etc.), manage infrastructure as code, as well as manage configuration & secrets. Moreover, the test takers are required to demonstrate their proficiency in designing & implementing CI/CD pipelines, deploying CI/CD tooling, and securing the deployment pipeline.

  • Implementation of Strategies for Service Monitoring

    Here the applicants will be asked to show that they have the capacity to manage the Stackdriver Logging and Stackdriver Monitoring platforms. They should also know how to manage application logs as well as application metrics using Stackdriver Monitoring. Additionally, the individuals will require the competency in implementing logging & monitoring access control.

  • Optimization of Service Performance

    Within this module, the learners need to show that they are capable of optimizing resource utilization, including determining resource costs, defining resource utilization levels, developing a plan to optimize areas of the lowest utilization/greatest cost, managing preemptible Virtual Machines, considering network pricing, and so on. They must also possess the skills in debugging application code as well as determining service performance issues.

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Google Professional Cloud DevOps Engineer Certified Professional salary

The average salary of a Google Professional Cloud DevOps Engineer Certified Expert in different regions is as follows:

  • Europe: 144,000 EURO
  • India: 25,00,000 INR
  • United State: 151,00 USD
  • England: 130,500 POUND

Google Professional Cloud DevOps Engineer Certification Path

The Google Professional Cloud DevOps Engineer Certification is the highest level of certification mainly focusing on the Solution Architect Professional. There is no prerequisite for this exam but still, it would be best to follow some sequence to prove immense knowledge as a Google Professional Cloud DevOps Engineer.

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

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

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

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