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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q39-Q44):
NEW QUESTION # 39
You recently migrated an ecommerce application to Google Cloud. You now need to prepare the application for the upcoming peak traffic season. You want to follow Google-recommended practices. What should you do first to prepare for the busy season?
- A. Pre-provision the additional compute power that was used last season, and expect growth.
- B. Migrate the application to Cloud Run, and use autoscaling.
- C. Load test the application to profile its performance for scaling.
- D. Create a Terraform configuration for the application's underlying infrastructure to quickly deploy to additional regions.
Answer: C
NEW QUESTION # 40
You have an application running in Google Kubernetes Engine. The application invokes multiple services per request but responds too slowly. You need to identify which downstream service or services are causing the delay. What should you do?
- A. Create a Dataflow pipeline to analyze service metrics in real time.
- B. Analyze VPC flow logs along the path of the request.
- C. Investigate the Liveness and Readiness probes for each service.
- D. Use a distributed tracing framework such as OpenTelemetry or Stackdriver Trace.
Answer: A
NEW QUESTION # 41
You are designing a system with three different environments: development, quality assurance (QA), and production.
Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?
- A. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:
different branches are different environments
Application (app source code) repositories are separated: different branches are different features - B. Cloud Infrastructure (Terraform) repository is shared: different branches are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environments Application (app source code) repository is shared: different directories are different features
- C. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environments Application (app source code) repositories are separated: different branches are different features
- D. Cloud Infrastructure (Terraform) repositories are separated: different branches are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:
different overlay directories are different environments
Application (app source code) repositories are separated: different branches are different features
Answer: A
Explanation:
The correct answer is B, Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.
This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:
For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift. You can also use Terraform workspaces to isolate the state files for each environment12.
For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments. You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch. You can also use tags or labels to trigger deployments to different environments5 .
Reference:
1: Best practices for using Terraform | Google Cloud
2: Terraform Recommended Practices - Part 1 | Terraform - HashiCorp Learn
3: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud
5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog
6: GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud
NEW QUESTION # 42
Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?
- A. Configure the Horizontal Pod Autoscaler and enable the cluster autoscaler
- B. Configure the Horizontal Pod Autoscaler but keep the node pool size static
- C. Configure the Vertical Pod Autoscaler but keep the node pool size static
- D. Configure the Vertical Pod Autoscaler and enable the cluster autoscaler
Answer: A
NEW QUESTION # 43
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_datasetYou need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?
- A. Create an aggregated Iog sink in the Dev and Prod folders
- B. Create a log sink in each project
- C. Create two aggregated log sinks at the organization level, and filter by project ID
- D. Create a single aggregated log sink at the organization level.
Answer: D
Explanation:
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export.
Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.
NEW QUESTION # 44
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