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Answer: Update SQL pipelines to run as batch queries, and run ad-hoc queries as interactive query jobs.
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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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?
A
Increase the slot capacity of the project with a baseline of 0 and a maximum reservation size of 3000.
B
Update SQL pipelines to run as batch queries, and run ad-hoc queries as interactive query jobs.
C
Increase the slot capacity of the project with a baseline of 2000 and a maximum reservation size of 3000.
D
Update both SQL pipelines and ad-hoc queries to run as interactive query jobs.
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