Users are reporting that some BigQuery queries are taking much longer than expected, but others are fast. You need to identify whether the slow queries are due to inefficient query patterns or resource contention. What should you do?

Ask the users to rewrite their queries to be more efficient.

Look at the Cloud Logging logs for the slow queries to see if there were any errors.

Use the BigQuery admin panel to review the execution details of the slow queries, including the number of slots used and the query plan.

Check the Cloud Monitoring dashboard for the BigQuery slot usage to see if there is overall contention.

A data engineer accidentally submitted a long-running BigQuery query that is consuming excessive slots. What is the quickest way to stop this query?

Delete the dataset that the query reads from

Cancel the job from the BigQuery Job History page

Your organization plans to run a large-scale data processing workload next month. You need to ensure that you have enough BigQuery slots to handle the expected demand without exceeding your quota. What should you do?

Request a quota increase for BigQuery API requests without analyzing the expected slot usage.

Rely on the on-demand pricing model and hope that the default quota is sufficient.

Create a BigQuery reservation with a fixed number of slots based on the expected peak usage and monitor the reservation's utilization.

Use Cloud Scheduler to run a script that checks the current slot usage every hour and alerts you if it is high.

Your monthly Google Cloud bill shows a significant increase in BigQuery costs, but you did not intentionally run more queries. You need to identify the cause of the billing discrepancy. What should you do first?

Contact Google Cloud support and ask them to investigate the billing discrepancy.

Check the Cloud Billing report to see the total cost, but do not dig into the details.

Look at the Cloud Logging logs for BigQuery to see if there were any errors that might have caused retries.

Use the BigQuery admin panel to review the job history and identify any unexpected or large queries.

Google CloudProfessional Data Engineer

Domain 5Objective 4

5.4 Monitoring and Troubleshooting Processes PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 2)

Part of the Maintaining and automating data workloads domain, which accounts for ~18% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~9–14 in this domain), expect 2–3 from this objective — we provide 31 practice questions to prepare you well beyond it. (estimate)

31questions here

7free pages

9concepts

~18%of the exam

Questions 6–10

6foundation · easy
A data engineer wants to be notified immediately when a Cloud Dataflow pipeline's lag exceeds a threshold. Which Cloud Monitoring feature should be used to create this notification?
Select an answer first
7expert · hard
Users are reporting that some BigQuery queries are taking much longer than expected, but others are fast. You need to identify whether the slow queries are due to inefficient query patterns or resource contention. What should you do?
Select an answer first
8foundation · easy
A data engineer accidentally submitted a long-running BigQuery query that is consuming excessive slots. What is the quickest way to stop this query?
Select an answer first
9application · medium
Your organization plans to run a large-scale data processing workload next month. You need to ensure that you have enough BigQuery slots to handle the expected demand without exceeding your quota. What should you do?
Select an answer first
10application · medium
Your monthly Google Cloud bill shows a significant increase in BigQuery costs, but you did not intentionally run more queries. You need to identify the cause of the billing discrepancy. What should you do first?
Select an answer first

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