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Preparation Process

The best way to prepare for the Google Professional Cloud DevOps Engineer certification exam is to explore the training tools offered on its official website. The candidates can start their preparation process by reviewing the topics in the study guide. This will give them an idea of what they need to cover studying for the test and plan their learning time properly.

The official platform recommends that the potential individuals complete the Professional DevOps Engineer learning path, which includes in-person classes, online training, and hands-on labs for a better understating of the exam content. The courses within this path cover each aspect of the certification test in depth improving the candidates’ chances of passing the exam at the first attempt. Apart from the learning path, the applicants can take advantage of additional resources such as Google Cloud documentation and Google Cloud solutions. There is also the option of signing up for a dedicated webinar to learn valuable prep tips from the Google experts. At the end of your preparation, use the official sample questions to familiarize yourself with the format of exam questions and check the level of readiness.

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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.

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

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Preparation Resources for Certification Test

Passing this Google exam is possible from the first attempt if you combine different preparation resources such as the ones offered by the vendor and the books available on Amazon. In case you’re on the lookout for the most effective revision materials, here’s our choice:

  • Google Cloud DevOps Engineer Learning Path

    The vendor has developed a comprehensive learning path for candidates who want to pass the Professional Cloud DevOps Engineer exam. It includes several courses that will take the applicants through all the steps necessary to validate the necessary abilities to get the passing score from the first attempt. In particular, this learning kit includes the following training sessions:

    • The Google Cloud Fundamentals: Core Infrastructure class focuses on helping the candidates learn about storage and computing services which are included in Google Cloud Pack. Therefore, the exam-takers will learn how to use the BigQuery, App Engine, Cloud SQL and Storage, and Google Kubernetes Engine features.
    • The training dedicated to the development of a Google SRE culture helps the candidates gain advanced skills in providing production support and minimizing the gap between operations and development. During this course, applicants will learn the secrets of Site Reliability Engineering and how they can benefit from creating a Google SRE culture.
    • The Architecting with Google Cloud: Design and Process class helps the test-takers learn how to deploy Google Cloud features in a cost-effective and performant way.
    • The final training session included in this learning path is dedicated to monitoring, logging, and observability in Google Cloud. It teaches the candidates how to troubleshoot and improve Google Cloud infrastructure performance.

    Once the candidates go through the courses mentioned above, they will gain several skill badges that consolidate their skills in DevOps engineering. These badges are as follows:

    • The skill badge that validates the ability to use Google Cloud for performing foundational infrastructure tasks. It helps the candidates learn how to manage important application services such as Cloud Functions or Stackdriver.
    • The second badge is related to the implementation of DevOps features in Google Cloud. In particular, it will help the candidates improve their software delivery capacity with the help of Google Cloud and achieve a higher speed, availability, and stability.
    • The last badge helps the candidates understand how to use the Google Cloud Operations Suite for monitoring and logging. Thus, the applicants will learn how to use Google Cloud’s ins and outs for generating healthier and improved applications.

    Without doubt, a candidate who goes through this learning process provided by Google will have significantly higher chances to get certified in one go.

  • Google Cloud Professional Cloud DevOps Engineer Exam – All in One Guide: Get Certified Efficiently in Google Cloud! (Google Cloud Certification Series Book 7)

    This book is available on Amazon in Kindle format and has been published by Joseph Holbrook. Also, the candidates can buy it for almost $12. This guide follows all the topics tested in the certification exam and helps the applicants sharpen their skills in managing Google Cloud DevOps Engineer features. Besides, it helps entrants to fully prepare for the designation test as it includes more than 100 questions and detailed answers for each of them. Thus, the exam-takers will get used to the test structure and difficulty level. Plus, anyone who buys this guide will receive free access to additional learning resources that will consolidate their knowledge of Google Cloud apps and features.

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

SectionWeightObjectives
Cloud Infrastructure Automation24%- Deployment strategies
  • 1. Rolling updates with Kubernetes
  • 2. Blue-green deployments
  • 3. Feature flags with Firebase Remote Config or LaunchDarkly
  • 4. Canary releases and progressive rollouts
- Infrastructure as Code (IaC)
  • 1. Implementing immutable infrastructure patterns
  • 2. Implementing with Terraform
  • 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
CI/CD Pipeline Development26%- 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. Creating optimized Docker images (multi-stage builds)
  • 2. Managing container registries
  • 3. Implementing container security best practices
- Designing and implementing CI/CD pipelines
  • 1. Setting up artifact management with Artifact Registry
  • 2. Configuring build caching strategies
  • 3. Implementing build automation scripts
  • 4. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
  • 5. Configuring build triggers and webhooks
Reliability and Site Reliability Engineering (SRE)24%- 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
- Monitoring and observability
  • 1. Configuring Cloud Monitoring and Logging
  • 2. Creating dashboards for service health visibility
  • 3. Setting up alerting policies and incident management
  • 4. Implementing distributed tracing with Cloud Trace
- Incident management
  • 1. Implementing on-call procedures
  • 2. Post-incident reviews and blameless postmortems
  • 3. Configuring automated incident response
Monitoring, Logging, and Debugging12%- Debugging and troubleshooting
  • 1. Analyzing performance profiles with Cloud Profiler
  • 2. Debugging with Cloud Trace distributed tracing
  • 3. Using Cloud Debugger and Error Reporting
- Cloud Operations Suite
  • 1. Implementing Cloud Logging with log sinks
  • 2. Configuring Cloud Monitoring metrics
  • 3. Creating uptime checks and alerts
- Log management
  • 1. Structuring logs for efficient querying
  • 2. Implementing log-based metrics
  • 3. Configuring log retention policies
Microservices Architecture14%- Container orchestration with GKE
  • 1. Managing pod lifecycle and resource quotas
  • 2. Configuring node pools and auto-scaling
  • 3. Implementing workload deployment and scaling
  • 4. Designing Kubernetes cluster architectures
- API management
  • 1. Implementing API versioning strategies
  • 2. Exposing APIs with Cloud Endpoints / Apigee
- Service mesh and networking
  • 1. Implementing service-to-service authentication
  • 2. Configuring traffic management and load balancing
  • 3. Implementing Anthos Service Mesh / Istio

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