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Google Professional Data Engineer

Google Professional Data Engineer

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The data analyst team at your company uses BigQuery for both ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. Recently, the addition of many non time-sensitive SQL pipelines has led to frequent quota errors, with logs showing around 1500 queries running concurrently during peak hours. What is the best approach to alleviate these quota errors?

Real Exam



Explanation:

The correct answer is B: Update SQL pipelines to run as batch queries, and run ad-hoc queries as interactive query jobs. This approach effectively manages concurrency by scheduling non time-sensitive batch queries during off-peak hours, thus reducing peak-time concurrency. Interactive query jobs are better suited for ad-hoc queries, allowing for optimized resource allocation.

  • Option A is incorrect because merely increasing slot capacity without optimizing query types and scheduling may not efficiently manage the workload.
  • Option C might offer some relief but isn't as efficient as optimizing query types and scheduling.
  • Option D is not ideal because running all queries as interactive may not be suitable for non time-sensitive operations, leading to inefficient resource use.
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