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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
You use Cloud Build to build your application. You want to reduce the build time while minimizing cost and development effort. What should you do?

A. Run multiple Jenkins agents to parallelize the build.
B. Use larger Cloud Build virtual machines (VMs) by using the machine-type option.
C. Use multiple smaller build steps to minimize execution time.
D. Use Cloud Storage to cache intermediate artifacts.

Answer: C

Explanation:
https://cloud.google.com/storage/docs/best-practices
. https://cloud.google.com/build/docs/speeding-up-builds#caching_directories_with_google_cloud_storage Caching directories with Google Cloud Storage To increase the speed of a build, reuse the results from a previous build. You can copy the results of a previous build to a Google Cloud Storage bucket, use the results for faster calculation, and then copy the new results back to the bucket. Use this method when your build takes a long time and produces a small number of files that does not take time to copy to and from Google Cloud Storage.
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NEW QUESTION # 153
You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?

A. Monitor results of Stackdriver Trace to determine the required amount of resources.
B. Validate that the resource requirements are within the available quota limits of each region.
C. Deploy the service in one region and use a global load balancer to route traffic to this region.
D. Use the n1-highcpu-96 machine type in the configuration of the MIG.

Answer: C

NEW QUESTION # 154
You are responsible for the reliability of a high-volume enterprise application. A large number of users report that an important subset of the application's functionality - a data intensive reporting feature - is consistently failing with an HTTP 500 error. When you investigate your application's dashboards, you notice a strong correlation between the failures and a metric that represents the size of an internal queue used for generating reports. You trace the failures to a reporting backend that is experiencing high I/O wait times. You quickly fix the issue by resizing the backend's persistent disk (PD). How you need to create an availability Service Level Indicator (SLI) for the report generation feature. How would you define it?

A. As the I/O wait times aggregated across all report generation backends
B. As the reporting backend PD throughout capacity compared to a known-good threshold
C. As the application's report generation queue size compared to a known-good threshold
D. As the proportion of report generation requests that result in a successful response

Answer: C

NEW QUESTION # 155
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. 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.
B. Configure Config Connector with the GitHub repository. When there is a change in the repository, use Config Connector to apply the change.
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.
D. When there is a change in GitHub, use a web hook to send a request to Anthos Service Mesh, and apply the change.

Answer: C

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 # 156
Your development team has created a new version of their service's API. You need to deploy the new versions of the API with the least disruption to third-party developers and end users of third-party installed applications. What should you do?

A. Introduce the new version of the API.
Announce deprecation of the old version of the API.
Deprecate the old version of the API.
Contact remaining users of the old API.
Provide best effort support to users of the old API.
Turn down the old version of the API.
B. Announce deprecation of the old version of the API.
Contact remaining users on the old API.
Introduce the new version of the API.
Deprecate the old version of the API.
Provide best effort support to users of the old API.
Turn down the old version of the API.
C. Announce deprecation of the old version of the API.
Introduce the new version of the API.
Contact remaining users on the old API.
Deprecate the old version of the API.
Turn down the old version of the API.
Provide best effort support to users of the old API.
D. Introduce the new version of the API.
Contact remaining users of the old API.
Announce deprecation of the old version of the API.
Deprecate the old version of the API.
Turn down the old version of the API.
Provide best effort support to users of the old API.

Answer: C

NEW QUESTION # 157
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