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Answer: They can increase the maximum bound of the SQL endpoint’s scaling range.
The correct answer is B. When many members of the team are running small queries simultaneously on the same SQL endpoint, the issue is related to concurrency. To handle this, increasing the maximum bound of the SQL endpoint’s scaling range allows the system to scale out and add more clusters to manage the increased load. This helps in distributing the queries more efficiently and reducing query latency. Increasing cluster size (option A) is more effective for sequentially run queries, while scaling out (option B) is better for concurrently run queries.
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In a scenario where a data analysis team has observed that their Databricks SQL queries are executing slower than expected when connected to their always-on SQL endpoint, they have reported that this performance issue arises particularly when multiple team members are executing small queries at the same time. Seeking assistance, they approach the data engineering team to resolve this bottleneck. Upon investigation, the data engineering team determines that each of these queries utilizes the same SQL endpoint. Which of the following strategies can the data engineering team adopt to enhance the query latency for the team?
A
They can increase the cluster size of the SQL endpoint.
B
They can increase the maximum bound of the SQL endpoint’s scaling range.
C
They can turn on the Auto Stop feature for the SQL endpoint.
D
They can turn on the Serverless feature for the SQL endpoint.
E
They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to 'Reliability Optimized.'