Databricks Certified Data Engineer - Associate
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A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint. Which of the following approaches can the data engineering team use to improve the latency of the team's queries?
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KKeng
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."
Explanation:
Explanation
When many team members are running small queries simultaneously on the same SQL endpoint, the issue is likely related to concurrency limitations. The SQL endpoint has a scaling range that determines how many clusters can be created to handle concurrent queries.
Let's analyze each option:
A. Increase the cluster size of the SQL endpoint - This would increase the resources of a single cluster, but doesn't address the concurrency issue when many users are running queries simultaneously. Larger clusters help with complex queries, not concurrent small queries.
B. Increase the maximum bound of the SQL endpoint's scaling range - ✅ CORRECT. This allows the SQL endpoint to create more clusters to handle concurrent queries. By increasing the maximum number of clusters in the scaling range, more users can run queries simultaneously without waiting for resources.
C. Turn on the Auto Stop feature for the SQL endpoint - This would automatically stop the endpoint after a period of inactivity, which doesn't help with performance during active use and could actually increase latency when queries need to restart the endpoint.
D. Turn on the Serverless feature for the SQL endpoint - While Serverless can help with resource management, it doesn't directly address the specific issue of concurrent small queries. Serverless is more about automatic scaling and cost optimization.
E. Turn on Serverless feature and change Spot Instance Policy - This is overly specific and not the best solution for concurrent small queries. Spot instances are about cost optimization, not concurrency.
Key Insight: The problem is concurrent small queries from many team members. SQL endpoints in Databricks can scale out by creating additional clusters when the concurrency limit is reached. Increasing the maximum bound of the scaling range allows more clusters to be created, thus handling more concurrent queries efficiently.
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