Google Professional-Cloud-Developer Actual PDF : Google Certified Professional - Cloud Developer

You are designing a resource-sharing policy for applications used by different teams in a Google Kubernetes Engine cluster. You need to ensure that all applications can access the resources needed to run. What should you do? (Choose two.)

• A. Use the Anthos Policy Controller to enforce label annotations on all namespaces. Use taints and tolerations to allow resource sharing for namespaces.
• B. Create a LimitRange to specify the default compute resource requirements for each namespace.
• C. Specify the resource limits and requests in the object specifications.
• D. Create a namespace for each team, and attach resource quotas to each namespace.
• E. Create a Kubernetes service account (KSA) for each application, and assign each KSA to the namespace.

Your application is built as a custom machine image. You have multiple unique deployments of the machine image. Each deployment is a separate managed instance group with its own template. Each deployment requires a unique set of configuration values. You want to provide these unique values to each deployment but use the same custom machine image in all deployments. You want to use out-of-the-box features of Compute Engine.
What should you do?

• A. Place the unique configuration values in the persistent disk.
• B. Place the unique configuration values in the instance template startup script.
• C. Place the unique configuration values in the instance template instance metadata.
• D. Place the unique configuration values in a Cloud Bigtable table.

Your teammate has asked you to review the code below. Its purpose is to efficiently add a large number of small rows to a BigQuery table.

Which improvement should you suggest your teammate make?

• A. Write each row to a Cloud Storage object, then load into BigQuery.
• B. Perform the inserts in parallel by creating multiple threads.
• C. Write each row to a Cloud Storage object in parallel, then load into BigQuery.
• D. Include multiple rows with each request.

You deployed a new application to Google Kubernetes Engine and are experiencing some performance degradation. Your logs are being written to Cloud Logging, and you are using a Prometheus sidecar model for capturing metrics. You need to correlate the metrics and data from the logs to troubleshoot the performance issue and send real-time alerts while minimizing costs.
What should you do?

• A. Create custom metrics from the Cloud Logging logs, and use Prometheus to import the results using the Cloud Monitoring REST API.
• B. Export the Cloud Logging logs and stream the Prometheus metrics to BigQuery. Run a recurring query to join the results, and send notifications using Cloud Tasks.
• C. Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics.Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.
• D. Export the Cloud Logging logs and the Prometheus metrics to Cloud Bigtable. Run a query to join the results, and analyze in Google Data Studio.

You have recently instrumented a new application with OpenTelemetry, and you want to check the latency of your application requests in Trace. You want to ensure that a specific request is always traced. What should you do?

• A. Use the Trace API to apply custom attributes to the trace.
• B. Wait 10 minutes, then verify that Trace captures those types of requests automatically.
• C. Add the X-Cloud-Trace-Context header to the request with the appropriate parameters.
• D. Write a custom script that sends this type of request repeatedly from your dev project.