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Free Professional Cloud Developer Exam Braindumps
Professional Cloud Developer Questions 91–95 (page: 20)

Updated On: 10-Feb-2026
Page 20 of 52
PDF with 379 Questions

Your team develops services that run on Google Kubernetes Engine. You need to standardize their log data using Google-recommended best practices and make the data more useful in the fewest number of steps. What should you do? (Choose two.)

  1. Create aggregated exports on application logs to BigQuery to facilitate log analytics.
  2. Create aggregated exports on application logs to Cloud Storage to facilitate log analytics.
  3. Write log output to standard output (stdout) as single-line JSON to be ingested into Cloud Logging as structured logs.
  4. Mandate the use of the Logging API in the application code to write structured logs to Cloud Logging.
  5. Mandate the use of the Pub/Sub API to write structured data to Pub/Sub and create a Dataflow streaming pipeline to normalize logs and write them to BigQuery for analytics.

Answer(s): A,E



You are designing a deployment technique for your new applications on Google Cloud. As part of your deployment planning, you want to use live traffic to gather performance metrics for both new and existing applications. You need to test against the full production load prior to launch. What should you do?

  1. Use canary deployment
  2. Use blue/green deployment
  3. Use rolling updates deployment
  4. Use A/B testing with traffic mirroring during deployment

Answer(s): A


Reference:

https://cloud.google.com/architecture/application-deployment-and-testing-strategies



You support an application that uses the Cloud Storage API. You review the logs and discover multiple HTTP 503 Service Unavailable error responses from the API. Your application logs the error and does not take any further action. You want to implement Google-recommended retry logic to improve success rates. Which approach should you take?

  1. Retry the failures in batch after a set number of failures is logged.
  2. Retry each failure at a set time interval up to a maximum number of times.
  3. Retry each failure at increasing time intervals up to a maximum number of tries.
  4. Retry each failure at decreasing time intervals up to a maximum number of tries.

Answer(s): C



You need to redesign the ingestion of audit events from your authentication service to allow it to handle a large increase in traffic. Currently, the audit service and the authentication system run in the same Compute Engine virtual machine. You plan to use the following Google Cloud tools in the new architecture:
•Multiple Compute Engine machines, each running an instance of the authentication service
•Multiple Compute Engine machines, each running an instance of the audit service
•Pub/Sub to send the events from the authentication services.

How should you set up the topics and subscriptions to ensure that the system can handle a large volume of messages and can scale efficiently?

  1. Create one Pub/Sub topic. Create one pull subscription to allow the audit services to share the messages.
  2. Create one Pub/Sub topic. Create one pull subscription per audit service instance to allow the services to share the messages.
  3. Create one Pub/Sub topic. Create one push subscription with the endpoint pointing to a load balancer in front of the audit services.
  4. Create one Pub/Sub topic per authentication service. Create one pull subscription per topic to be used by one audit service.
  5. Create one Pub/Sub topic per authentication service. Create one push subscription per topic, with the endpoint pointing to one audit service.

Answer(s): D



You are developing a marquee stateless web application that will run on Google Cloud. The rate of the incoming user traffic is expected to be unpredictable, with no traffic on some days and large spikes on other days. You need the application to automatically scale up and down, and you need to minimize the cost associated with running the application. What should you do?

  1. Build the application in Python with Firestore as the database. Deploy the application to Cloud Run.
  2. Build the application in C# with Firestore as the database. Deploy the application to App Engine flexible environment.
  3. Build the application in Python with Cloud SQL as the database. Deploy the application to App Engine standard environment.
  4. Build the application in Python with Firestore as the database. Deploy the application to a Compute Engine managed instance group with autoscaling.

Answer(s): C



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