Google Professional Data Engineer Question 259
Select 4Google Cloud PlatformYou are managing a Google Cloud project that processes large datasets using BigQuery. Recently, your team encountered a 'Quota exceeded' error during query execution. Which actions can you take to troubleshoot and resolve this issue?
- A
Check the BigQuery Quotas and Limits documentation to verify if the query exceeds any quota limits.
- B
Request a quota increase for BigQuery via the Google Cloud Console.
- C
Use the BigQuery INFORMATION_SCHEMA views to analyze query usage and optimize the query.
- D
Upgrade the project to a premium support plan to automatically bypass quota limits.
- E
Split the query into smaller parts and execute them sequentially to stay within quota limits.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
To troubleshoot and resolve 'Quota exceeded' errors in BigQuery, you should first understand the specific quota being exceeded by consulting the BigQuery Quotas and Limits documentation. If applicable, you can request a quota increase via the Google Cloud Console. Additionally, using INFORMATION_SCHEMA views to analyze and optimize queries can help reduce resource usage. In cases where quota limits cannot be increased, splitting queries into smaller parts is a practical workaround. Upgrading to a premium support plan does not impact quota enforcement.
- A. Correct.
This is correct. Reviewing the BigQuery Quotas and Limits documentation helps you understand which specific quota is being exceeded and plan appropriate remediation.
- B. Correct.
This is correct. If the exceeded quota can be increased (e.g., slots or API requests), you can request a quota increase through the Google Cloud Console.
- C. Correct.
This is correct. Using INFORMATION_SCHEMA views provides insights into query performance and resource usage, which can help optimize queries and reduce quota consumption.
- D. Incorrect.
This is incorrect. Upgrading to a premium support plan does not bypass quota limits. Quotas are enforced regardless of support plans.
- E. Correct.
This is correct. Splitting a large query into smaller parts and running them sequentially can help you stay within quota limits, especially for operations like daily query usage limits.