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.

Exam Details

The qualifying test for the Google Professional Cloud DevOps Engineer certification has the duration of 2 hours. The candidates will be asked to answer around 50 multiple-select and multiple-choice questions. The exam is available in the English language only. The students can take it either as an online proctored test or an onsite proctored exam at an authorized testing center. You can check the location of the nearest center in your region using the search on the official webpage. Each learner is required to pay the registration fee of $200 to schedule the exam.

If you don’t manage to pass the exam on the first try, you can retake it after 14 days. If you don't complete it for the second time, you will be required to wait for 60 days before you can sit for it for the third time. If you fail the third attempt, you will have to wait for 1 year before taking the test again.

Conclusion

Passing the Google Professional Cloud DevOps Engineer evaluation is not impossible as long as you combine different learning solutions. While the vendor offers different preparation resources for the exam-takers who want to get the passing score without stress, you shouldn’t stop here. You can also go as far as to explore the guides available on Amazon and enhance your skills for becoming a certified Cloud DevOps Engineer.

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Topics of Google Professional Cloud DevOps Engineer Exam

Candidates must know the exam topics before they start preparation because it will help them in hitting the core. Our Professional Cloud DevOps Engineer Dumps will include the following topics:

Applying site reliability engineering principles to a service

• Manage a service (e.g., introduce a new service, deploy it, maintain and retire it)
• 1.2 Manage service life cycle:
• Discover SLIs (availability, latency, etc.)
• Balance change, velocity, and reliability of the service:
• Define SLOs and understand SLAs
• Toil automation
• Agree to consequences of not meeting the error budget
• Construct feedback loops to decide what to build next

Building and implementing CI/CD pipelines for a service

• CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub
• Design CI/CD pipelines:
• Testing a new version with Spinnaker
• Configure deployment processes (e.g., approval flows)
• Deployment to hybrid and multi-cloud environments with Anthos, Spinnaker, Kubernetes
• Deployment strategies with Cloud Build, Spinnaker
• Artifact repositories with Container Registry
• Immutable artifacts with Container Registry
• Artifact versioning strategy with Cloud Build, Container Registry

Implementing service monitoring strategies

• Sending application logs directly to Stackdriver API with Stackdriver Logging
• Implementing a project-level / org-level export
• Use metric explorer for ad hoc metric analysis
• Enabling VPC flow logs
• Using basic vs. advanced logging filters
• Sending logs to an external logging platform
• Implementing logs-based metrics
• Understanding the logging exclusion vs. logging export
• Collecting third-party and structured logs with Stackdriver Logging, Fluentd
• Viewing logs in the GCP Console
• Set ACL to restrict access to audit logs with IAM, Stackdriver Logging
• Set ACL to allow metric writing for custom metrics with IAM, Stackdriver Monitoring
• Manage application logs:
• Set ACL to restrict export configuration with IAM, Stackdriver Logging
• Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd
• Collecting metrics from Compute Engine
• Viewing export logs in Cloud Storage and BigQuery
• Collecting GKE/Kubernetes metrics
• Selecting the options for logging export
• Enabling data access logs (e.g., Cloud Audit Logs)

Optimizing service performance

• Interpret service mesh telemetry
• Develop a plan to optimize areas of greatest cost or lowest utilization
• TCO considerations
• identify resource costs
• Manage preemptible VMs
• Troubleshoot network issues (e.g., VPC flow logs, firewall logs, latency, view network details)
• Consider network pricing
• Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)
• Troubleshoot issues with the image/OS
• Work with committed-use discounts
• Identify service performance issues:
• Utilize Stackdriver to identify cloud resource utilization
• Identify resource utilization levels
• Utilize Stackdriver Trace/Profiler to profile performance characteristics

Managing service incidents

• Identify probable causes of service failure
• Scaling response team and delegation
• Record major changes in incident state (When mitigated? When all clear? etc.)
• Rotate/hand over roles
• Identify alternatives to mitigate the issue
• Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)
• Evaluate symptoms against probable causes; the rank probability of cause based on observed behavior
• Perform an investigation to isolate the most likely actual cause
• Manage stakeholder relationships
• Coordinate roles and implement communication channels during a service incident:
• Define roles (incident commander, communication lead, operations lead)
• Provide regular status updates, internal and external
• Avoid exhaustion/burnout
• Handle requests for impact assessment

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

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