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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
You support a large service with a well-defined Service Level Objective (SLO). The development team deploys new releases of the service multiple times a week. If a major incident causes the service to miss its SLO, you want the development team to shift its focus from working on features to improving service reliability. What should you do before a major incident occurs?

Answer: A


NEW QUESTION # 72
You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using a CI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message

You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?

Answer: D

Explanation:
Explanation
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.


NEW QUESTION # 73
You need to define SLOs for a high-traffic web application. Customers are currently happy with the application performance and availability. Based on current measurement, the 90th percentile Of latency is 160 ms and the 95th percentile of latency is 300 ms over a 28-day window. What latency SLO should you publish?

Answer: B


NEW QUESTION # 74
Your company runs applications in Google Kubernetes Engine (GKE). Several applications rely on ephemeral volumes. You noticed some applications were unstable due to the DiskPressure node condition on the worker nodes. You need to identify which Pods are causing the issue, but you do not have execute access to workloads and nodes.
What should you do?

Answer: C

Explanation:
The correct answer is A. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1.You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.
The other options are incorrect because they require execute access to the workloads or nodes, which you do not have.The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.


NEW QUESTION # 75
You are investigating issues in your production application that runs on Google Kubernetes Engine (GKE). You determined that the source Of the issue is a recently updated container image, although the exact change in code was not identified. The deployment is currently pointing to the latest tag. You need to update your cluster to run a version of the container that functions as intended. What should you do?

Answer: A


NEW QUESTION # 76
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