Test Structure

The candidates who want to take this Google exam will have two hours to answer all questions. Even though the vendor doesn’t give details on the total number of questions that the examinees will receive, they should be prepared to solve multiple-choice and multiple-answer inquiries. Besides, the test is delivered in the English language only. As for the registration fee, the test-takers will need to pay $200 to take it. Additional taxes may apply depending on the candidate’s profile and chosen delivery method. By and large, the applicants have two options to take the official exam. They can choose to take it online from any remote location that they prefer. If they choose this option, the candidates should read carefully what the testing requirements are. In case applicants prefer to be present in a classroom when they take the actual testing, then they can search for a test center that is closest to their location. Also, Google doesn’t have any prerequisites for the candidates to be eligible for the evaluation. Still, it recommends that the candidates for the Professional Cloud DevOps Engineer exam should have at least 3 years of experience in the industry including a minimum of one year of experience in managing and developing solutions on GCP.

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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:

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

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
CI/CD Pipeline Development	26%	- 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 - Containerization and Docker 1. Managing container registries 2. Creating optimized Docker images (multi-stage builds) 3. Implementing container security best practices - CI/CD best practices 1. Configuring quality gates and code coverage 2. Implementing shift-left testing 3. Managing secrets in CI/CD pipelines
Monitoring, Logging, and Debugging	12%	- 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. Debugging with Cloud Trace distributed tracing 2. Analyzing performance profiles with Cloud Profiler 3. Using Cloud Debugger and Error Reporting - Log management 1. Configuring log retention policies 2. Structuring logs for efficient querying 3. Implementing log-based metrics
Microservices Architecture	14%	- Service mesh and networking 1. Configuring traffic management and load balancing 2. Implementing Anthos Service Mesh / Istio 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. Managing pod lifecycle and resource quotas 2. Implementing workload deployment and scaling 3. Designing Kubernetes cluster architectures 4. Configuring node pools and auto-scaling
Cloud Infrastructure Automation	24%	- Deployment strategies 1. Rolling updates with Kubernetes 2. Feature flags with Firebase Remote Config or LaunchDarkly 3. Canary releases and progressive rollouts 4. Blue-green deployments - Configuration management 1. Implementing configuration drift detection 2. Managing secrets with Secret Manager 3. Using Ansible for configuration management - Infrastructure as Code (IaC) 1. Implementing immutable infrastructure patterns 2. Managing infrastructure modules and state 3. Implementing with Terraform
Reliability and Site Reliability Engineering (SRE)	24%	- SLOs, SLIs, and SLAs 1. Error budget policies and management 2. Defining and implementing Service Level Objectives 3. Setting appropriate Service Level Agreements 4. Creating and interpreting Service Level Indicators - Incident management 1. Implementing on-call procedures 2. Post-incident reviews and blameless postmortems 3. Configuring automated incident response - Monitoring and observability 1. Configuring Cloud Monitoring and Logging 2. Creating dashboards for service health visibility 3. Implementing distributed tracing with Cloud Trace 4. Setting up alerting policies and incident management