Google Cloud Certified - Professional Cloud DevOps Engineer : Professional-Cloud-DevOps-Engineer

  • Exam Code: Professional-Cloud-DevOps-Engineer
  • Exam Name: Google Cloud Certified - Professional Cloud DevOps Engineer Exam
  • Q & A: 208 Questions and Answers

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Google Professional Cloud DevOps Engineer exam cost is USD 200, for more information related to the Google Professional Cloud DevOps Engineer exam, please visit Google Website. Additional costs of Professional Cloud DevOps Engineer exam dumps pdf and Professional Cloud DevOps Engineer practice test are not included in this price.

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

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

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

Building and implementing CI/CD pipelines for a service

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

Implementing service monitoring strategies

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

Optimizing service performance

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

Managing service incidents

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

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

SectionWeightObjectives
Optimizing performance and cost17%- Balance cost, performance, and reliability
  • 1. Make tradeoff decisions based on data
  • 2. Plan capacity and scaling
  • 3. Optimize over time
- Improve system performance
  • 1. Use caching and content delivery
  • 2. Optimize resource allocation
  • 3. Tune applications and infrastructure
- Apply FinOps practices
  • 1. Right-size resources and eliminate waste
  • 2. Monitor and analyze cloud costs
  • 3. Use spot instances and committed use discounts
Building and implementing CI/CD pipelines25%- Optimize and maintain pipelines
  • 1. Handle rollbacks and versioning
  • 2. Monitor pipeline performance
  • 3. Improve speed and reliability
- Design CI/CD pipelines
  • 1. Support hybrid/multi-cloud deployment
  • 2. Integrate artifact management
  • 3. Plan pipelines for apps, infrastructure, ML workloads
- Implement and secure pipelines
  • 1. Enforce security scanning and compliance checks
  • 2. Manage pipeline secrets and credentials
  • 3. Automate build, test, and deployment stages
Applying site reliability engineering practices18%- Define reliability goals
  • 1. Use error budgets to balance change and stability
  • 2. Establish SLIs, SLOs, SLAs
  • 3. Align reliability with business needs
- Design for resilience
  • 1. Automate remediation and self-healing
  • 2. Test failure scenarios and recovery
  • 3. Plan redundancy and fault tolerance
- Manage incidents and outages
  • 1. Perform postmortems and root cause analysis
  • 2. Detect and respond to issues quickly
  • 3. Implement improvements to prevent recurrence
Implementing observability and troubleshooting20%- Optimize observability strategy
  • 1. Reduce noise and prioritize signals
  • 2. Integrate observability into development
  • 3. Scale monitoring for large environments
- Build monitoring and logging systems
  • 1. Collect and analyze metrics, logs, traces
  • 2. Create dashboards and alerts
  • 3. Use Cloud Monitoring, Cloud Logging, Cloud Trace
- Troubleshoot complex issues
  • 1. Isolate problems across layers
  • 2. Trace requests end-to-end
  • 3. Analyze performance and latency
Bootstrapping and maintaining a Google Cloud organization20%- Set up governance and compliance
  • 1. Implement organization policies
  • 2. Configure audit logging and monitoring
  • 3. Manage resource quotas and limits
- Design resource hierarchy
  • 1. Plan shared networking and connectivity
  • 2. Organize resources by project/folder/application
  • 3. Define IAM policies and access control
- Manage configuration and deployment
  • 1. Use Infrastructure as Code tools
  • 2. Enforce consistency across environments
  • 3. Automate resource provisioning

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