Google Cloud Professional Data Engineer — Question 13

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Answer options

• A. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
• B. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
• C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
• D. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

Correct answer: C

Explanation

The correct answer is C because Cloud Dataflow is designed for handling streaming data and can automatically scale based on input volume, making it cost-effective and efficient. The other options involve Cloud Dataproc, which requires more manual intervention to manage resources and does not provide the same level of autoscaling capabilities as Cloud Dataflow.