最新のProfessional-Cloud-Developer問題集で試験を準備する

You have an application deployed in Google Kubernetes Engine (GKE) that reads and processes Pub/Sub messages. Each Pod handles a fixed number of messages per minute. The rate at which messages are published to the Pub/Sub topic varies considerably throughout the day and week, including occasional large batches of messages published at a single moment.
You want to scale your GKE Deployment to be able to process messages in a timely manner.
What GKE feature should you use to automatically adapt your workload?

• A. Vertical Pod Autoscaler in Auto mode
• B. Horizontal Pod Autoscaler based on an external metric
• C. Vertical Pod Autoscaler in Recommendation mode
• D. Horizontal Pod Autoscaler based on resources utilization

You are running a web application on Google Kubernetes Engine that you inherited. You want to determine whether the application is using libraries with known vulnerabilities or is vulnerable to XSS attacks. Which service should you use?

• A. Web Security Scanner
• B. Debugger
• C. Google Cloud Armor
• D. Error Reporting

Your team runs a Python job that reads millions of customer record files stored in a Cloud Storage bucket. To comply with regulatory requirements, you need to ensure that customer data is immediately deleted once the job is completed. You want to minimize the time required to complete this task. What should you do?

• A. Configure Object Lifecycle Management on the Cloud Storage bucket that deletes all the objects in the bucket at the end of the job execution.
• B. Add a final step in the job that deletes all the objects in the bucket in bulk by using batch requests to the Cloud Storage API.
• C. Remove the bucket from the Google Cloud console when the job is completed
• D. Use the gcloud CLI to execute the gcloud storage rm --recursive gs://BUCKET_NAME/ command.

Your team develops services that run on Google Cloud. You want to process messages sent to a Pub/Sub topic, and then store them. Each message must be processed exactly once to avoid duplication of data and any data conflicts. You need to use the cheapest and most simple solution. What should you do?

• A. Process the messages with a Dataflow streaming pipeline using Apache Beam's PubSubIO package, and write the output to storage.
• B. Process the messages with a Cloud Function, and write the results to a BigQuery location where you can run a job to deduplicate the data.
• C. Retrieve the messages with a Dataflow streaming pipeline, store them in Cloud Bigtable, and use another Dataflow streaming pipeline to deduplicate messages.
• D. Process the messages with a Dataproc job, and write the output to storage.

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?

• A. Retry each failure at increasing time intervals up to a maximum number of tries.
• B. Retry each failure at a set time interval up to a maximum number of times.
• C. Retry the failures in batch after a set number of failures is logged.
• D. Retry each failure at decreasing time intervals up to a maximum number of tries.