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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.

The Google Professional Cloud DevOps Engineer certification is designed to equip the specialists with the expertise required to perform the following technical tasks: applying the principles of site reliability engineering to services, optimizing service performance, implementing the strategies for service monitoring, building and implementing CI/CD pipelines for services, as well as managing service incidents. To get the certificate, the candidates need to complete a single exam covering a wide range of knowledge and skills.

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

Building and implementing CI/CD pipelines for a service

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

Implementing service monitoring strategies

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

Optimizing service performance

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

Managing service incidents

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

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

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