RELIABLE PROFESSIONAL-CLOUD-DEVOPS-ENGINEER EXAM VOUCHER - PROFESSIONAL-CLOUD-DEVOPS-ENGINEER LATEST EXAM NOTES

Reliable Professional-Cloud-DevOps-Engineer Exam Voucher - Professional-Cloud-DevOps-Engineer Latest Exam Notes

Reliable Professional-Cloud-DevOps-Engineer Exam Voucher - Professional-Cloud-DevOps-Engineer Latest Exam Notes

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Google Professional-Cloud-DevOps-Engineer Exam Tests the candidate’s knowledge in several areas, including cloud architecture, containerization, automation, monitoring, and security. Professional-Cloud-DevOps-Engineer exam is intended for professionals who have experience in designing and implementing DevOps solutions on the Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam is designed to validate the candidates’ knowledge and skills in deploying and managing applications on the Google Cloud Platform using DevOps methodologies. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is highly valued in the industry and is an excellent way for professionals to demonstrate their expertise in Google Cloud technologies.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

  • A. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: // [bucket] .
    srorage.googleapis.com/ [holnchart] as the Helm repository
  • B. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider
  • C. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
  • D. Store public and private charts in OCI format by using Artifact Registry

Answer: D

Explanation:
The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry's native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.


NEW QUESTION # 97
You use a multiple step Cloud Build pipeline to build and deploy your application to Google Kubernetes Engine (GKE). You want to integrate with a third-party monitoring platform by performing a HTTP POST of the build information to a webhook. You want to minimize the development effort. What should you do?

  • A. Create a Cloud Pub/Sub push subscription to the Cloud Build cloud-builds PubSub topic to HTTP POST the build information to a webhook.
  • B. Add logic to each Cloud Build step to HTTP POST the build information to a webhook.
  • C. Use Stackdriver Logging to create a logs-based metric from the Cloud Buitd logs. Create an Alert with a Webhook notification type.
  • D. Add a new step at the end of the pipeline in Cloud Build to HTTP POST the build information to a webhook.

Answer: A


NEW QUESTION # 98
You work for a global organization and are running a monolithic application on Compute Engine You need to select the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps You want to use historical system metncs to identify the machine type for the application to use You want to follow Google-recommended practices What should you do?

  • A. Create an Agent Policy to automatically install Ops Agent in all VMs
  • B. Use the Recommender API and apply the suggested recommendations
  • C. Review the Cloud Monitoring dashboard for the VM and choose the machine type with the lowest CPU utilization
  • D. Install the Ops Agent in a fleet of VMs by using the gcloud CLI

Answer: B

Explanation:
Explanation
The best option for selecting the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps is to use the Recommender API and apply the suggested recommendations.
The Recommender API is a service that provides recommendations for optimizing your Google Cloud resources, such as Compute Engine instances, disks, and firewalls. You can use the Recommender API to get recommendations for changing the machine type of your Compute Engine instances based on historical system metrics, such as CPU utilization. You can also apply the suggested recommendations by using the Recommender API or Cloud Console. This way, you can optimize CPU utilization by using the most suitable machine type for your application with minimal effort.


NEW QUESTION # 99
You need to enforce several constraint templates across your Google Kubernetes Engine (GKE) clusters. The constraints include policy parameters, such as restricting the Kubernetes API. You must ensure that the policy parameters are stored in a GitHub repository and automatically applied when changes occur. What should you do?

  • A. Configure Config Connector with the GitHub repository. When there is a change in the repository, use Config Connector to apply the change.
  • B. When there is a change in GitHub, use a web hook to send a request to Anthos Service Mesh, and apply the change.
  • C. Set up a GitHub action to trigger Cloud Build when there is a parameter change. In Cloud Build, run a gcloud CLI command to apply the change.
  • D. Configure Anthos Config Management with the GitHub repository. When there is a change in the repository, use Anthos Config Management to apply the change.

Answer: D

Explanation:
Explanation
The correct answer is C. Configure Anthos Config Management with the GitHub repository. When there is a change in the repository, use Anthos Config Management to apply the change.
According to the web search results, Anthos Config Management is a service that lets you manage the configuration of your Google Kubernetes Engine (GKE) clusters from a single source of truth, such as a GitHub repository1. Anthos Config Management can enforce several constraint templates across your GKE clusters by using Policy Controller, which is a feature that integrates the Open Policy Agent (OPA) Constraint Framework into Anthos Config Management2. Policy Controller can apply constraints that include policy parameters, such as restricting the Kubernetes API3. To use Anthos Config Management and Policy Controller, you need to configure them with your GitHub repository and enable the sync mode4. When there is a change in the repository, Anthos Config Management will automatically sync and apply the change to your GKE clusters5.
The other options are incorrect because they do not use Anthos Config Management and Policy Controller.
Option A is incorrect because it uses a GitHub action to trigger Cloud Build, which is a service that executes your builds on Google Cloud Platform infrastructure6. Cloud Build can run a gcloud CLI command to apply the change, but it does not use Anthos Config Management or Policy Controller. Option B is incorrect because it uses a web hook to send a request to Anthos Service Mesh, which is a service that provides a uniform way to connect, secure, monitor, and manage microservices on GKE clusters7. Anthos Service Mesh can apply the change, but it does not use Anthos Config Management or Policy Controller. Option D is incorrect because it uses Config Connector, which is a service that lets you manage Google Cloud resources through Kubernetes configuration. Config Connector can apply the change, but it does not use Anthos Config Management or Policy Controller.


NEW QUESTION # 100
You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?

  • A. Compare the canary with a new deployment of the previous production version.
  • B. Compare the canary with a new deployment of the current production version.
  • C. Compare the canary with the existing deployment of the current production version.
  • D. Compare the canary with the average performance of a sliding window of previous production versions.

Answer: B

Explanation:
https://cloud.google.com/architecture/automated-canary-analysis-kubernetes-engine-spinnaker
https://spinnaker.io/guides/user/canary/best-practices/#compare-canary-against-baseline-not-against-production


NEW QUESTION # 101
......

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