Your company uses BigQuery to store and analyze data. Upon submitting your query in BigQuery, the query fails with a quotaExceeded error. You need to diagnose the issue causing the error. What should you do? (Choose two.)
A.
Use BigQuery BI Engine to analyze the issue.
B.
Use the INFORMATION_SCHEMA views to analyze the underlying issue.
C.
Configure Cloud Trace to analyze the issue.
D.
Search errors in Cloud Audit Logs to analyze the issue.
E.
View errors in Cloud Monitoring to analyze the issue.
B & D.
Here's why these two methods are crucial for diagnosing quotaExceeded errors in BigQuery:
B. INFORMATION_SCHEMA Views: BigQuery's INFORMATION_SCHEMA provides metadata about datasets, tables, and jobs. Relevant views like JOBS_BY_PROJECT and JOBS_BY_USER can help you analyze recent queries, their resource consumption (bytes processed, slots used), and any errors encountered. This can reveal which queries are exceeding quotas and what type of quota (e.g., query size, daily limit) is being exceeded.
D. Cloud Audit Logs: Audit logs record all API calls and administrative actions within your GCP projects. By searching for quotaExceeded errors in the audit logs, you can see the exact error messages, timestamps, and potentially the queries that triggered the error. This helps pinpoint the specific resources and actions causing the issue.
a. Query INFORMATION_SCHEMA to identify requests that exceed quotas.
b. See the Cloud Audit Logs for more details on 'quotaExceeded' errors and the context in which they occur.
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