How to Prepare For Google Professional Cloud DevOps Engineer Exam

Preparation Guide for Google Professional Cloud DevOps Engineer Exam

Introduction

Google has designed a track for IT professionals to endorse as a cloud DevOps Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using the industry-standard methodology to conclude whether or not an aspirant meets Google's proficiency standards.

According to Google, a Google Professional Cloud DevOps Engineer Exam test facilitates organizations to influence Google Cloud technologies. By leveraging experience implementing VPCs, DevOps services, hybrid connectivity, and security for established DevOps architectures, this individual ensures successful cloud implementations using the Google Cloud Platform Console or the command-line interface.

Certification is evidence of your skills, expertise in those areas in which you like to work. If a candidate wants to work as Google Professional Cloud DevOps Engineer and prove his knowledge, certification is offered by Google. This Google Professional Cloud DevOps Engineer Certification helps a candidate to validates his skills in Google Professional Cloud DevOps Engineer Technology.

In this guide, we will cover the Google Professional Cloud DevOps Engineer practice exams, Google Professional Cloud DevOps Engineer Certified Professionals salary, and all aspects of the Google Professional Cloud DevOps Engineer Certification.

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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

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

Building and implementing CI/CD pipelines for a service

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

Implementing service monitoring strategies

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

Optimizing service performance

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

Managing service incidents

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

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

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