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

SectionWeightObjectives
Topic 1: Optimizing performance and cost17%- Balance cost, performance, and reliability
  • 1. Optimize over time
  • 2. Plan capacity and scaling
  • 3. Make tradeoff decisions based on data
- Apply FinOps practices
  • 1. Right-size resources and eliminate waste
  • 2. Monitor and analyze cloud costs
  • 3. Use spot instances and committed use discounts
- Improve system performance
  • 1. Use caching and content delivery
  • 2. Optimize resource allocation
  • 3. Tune applications and infrastructure
Topic 2: Bootstrapping and maintaining a Google Cloud organization20%- Manage configuration and deployment
  • 1. Use Infrastructure as Code tools
  • 2. Automate resource provisioning
  • 3. Enforce consistency across environments
- Set up governance and compliance
  • 1. Manage resource quotas and limits
  • 2. Configure audit logging and monitoring
  • 3. Implement organization policies
- Design resource hierarchy
  • 1. Define IAM policies and access control
  • 2. Plan shared networking and connectivity
  • 3. Organize resources by project/folder/application
Topic 3: Applying site reliability engineering practices18%- Design for resilience
  • 1. Test failure scenarios and recovery
  • 2. Plan redundancy and fault tolerance
  • 3. Automate remediation and self-healing
- Define reliability goals
  • 1. Use error budgets to balance change and stability
  • 2. Align reliability with business needs
  • 3. Establish SLIs, SLOs, SLAs
- Manage incidents and outages
  • 1. Detect and respond to issues quickly
  • 2. Perform postmortems and root cause analysis
  • 3. Implement improvements to prevent recurrence
Topic 4: Implementing observability and troubleshooting20%- Troubleshoot complex issues
  • 1. Analyze performance and latency
  • 2. Trace requests end-to-end
  • 3. Isolate problems across layers
- Optimize observability strategy
  • 1. Integrate observability into development
  • 2. Scale monitoring for large environments
  • 3. Reduce noise and prioritize signals
- Build monitoring and logging systems
  • 1. Use Cloud Monitoring, Cloud Logging, Cloud Trace
  • 2. Create dashboards and alerts
  • 3. Collect and analyze metrics, logs, traces
Topic 5: Building and implementing CI/CD pipelines25%- Design CI/CD pipelines
  • 1. Plan pipelines for apps, infrastructure, ML workloads
  • 2. Support hybrid/multi-cloud deployment
  • 3. Integrate artifact management
- Implement and secure pipelines
  • 1. Manage pipeline secrets and credentials
  • 2. Automate build, test, and deployment stages
  • 3. Enforce security scanning and compliance checks
- Optimize and maintain pipelines
  • 1. Monitor pipeline performance
  • 2. Improve speed and reliability
  • 3. Handle rollbacks and versioning

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
You encounter a large number of outages in the production systems you support. You receive alerts for all the outages that wake you up at night. The alerts are due to unhealthy systems that are automatically restarted within a minute. You want to set up a process that would prevent staff burnout while following Site Reliability Engineering practices. What should you do?

Answer: C

Explanation:
Eliminate bad monitoring : Unactionable alerts (i.e., spam)https://cloud.google.com/blog/products
/management-tools/meeting-reliability-challenges-with-sre-principles
agree with kyubiblaze about having to remove unactionable items aka spam: "good monitoring alerts on actionable problems" @https://cloud.google.com/blog/products/management-tools/meeting-reliability- challenges-with-sre-principles


NEW QUESTION # 44
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

Answer: D

Explanation:
https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics The Google Cloud HTTP Load Balancer (GCLB) provides metrics on the number of requests and the response latency for each backend service. These metrics can be used as custom metrics for the horizontal pod autoscaler (HPA) to scale the deployment based on the load. This is the correct solution to use an appropriate SLI for scaling.


NEW QUESTION # 45
You are deploying an application that needs to access sensitive information. You need to ensure that this information is encrypted and the risk of exposure is minimal if a breach occurs. What should you do?

Answer: B

Explanation:
Explanation
https://cloud.google.com/security-key-management


NEW QUESTION # 46
You support a popular mobile game application deployed on Google Kubernetes Engine (GKE) across several Google Cloud regions. Each region has multiple Kubernetes clusters. You receive a report that none of the users in a specific region can connect to the application. You want to resolve the incident while following Site Reliability Engineering practices. What should you do first?

Answer: D


NEW QUESTION # 47
You are configuring the frontend tier of an application deployed in Google Cloud The frontend tier is hosted in ngmx and deployed using a managed instance group with an Envoy-based external HTTP(S) load balancer in front The application is deployed entirely within the europe-west2 region: and only serves users based in the United Kingdom. You need to choose the most cost-effective network tier and load balancing configuration What should you use?

Answer: B


NEW QUESTION # 48
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