Free Access Google.Professional-Data-Engineer.v2023-09-14.q233 Practice Test (Page 36)

Question 171

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

A.Increase the cluster size with more non-preemptible workers.

B.Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

C.Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

D.Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Question 172

You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

A.Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name

B.Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name

C.Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

D.Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

Question 173

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.
You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

A.Use Cloud SQL for storage. Add secondary indexes to support query patterns.

B.Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

C.Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

D.Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

Question 174

What are two of the characteristics of using online prediction rather than batch prediction?

A.It is optimized to handle a high volume of data instances in a job and to run more complex models.

B.Predictions are returned in the response message.

C.Predictions are written to output files in a Cloud Storage location that you specify.

D.It is optimized to minimize the latency of serving predictions.

Question 175

Does Dataflow process batch data pipelines or streaming data pipelines?

A.Only Batch Data Pipelines

B.Both Batch and Streaming Data Pipelines

C.Only Streaming Data Pipelines

D.None of the above

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Question 171

Question 172

Question 173

Question 174

Question 175

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