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
This question tests your understanding of how to optimize SQL Endpoint (SQL Warehouse) performance for concurrent query execution.
Key context from the question:
- Many team members are running small queries simultaneously
- The issue occurs when many queries are running at the same time
- The SQL endpoint is always-on
Analysis of options:
A. They can increase the cluster size of the SQL endpoint.
- This would help with sequential query performance (scale up)
- For concurrent queries, you need more clusters, not bigger clusters
- ❌ Incorrect - This addresses individual query performance, not concurrent execution
B. They can increase the maximum bound of the SQL endpoint's scaling range.
- ✅ Correct - This allows the SQL endpoint to scale out (add more clusters) to handle concurrent queries
- Increasing the maximum bound enables more clusters to be provisioned when many users run queries simultaneously
- This is the proper solution for concurrent workload scaling
C. They can turn on the Auto Stop feature for the SQL endpoint.
- ❌ Incorrect - Auto Stop saves costs by stopping idle endpoints, but doesn't improve performance for active concurrent queries
- This might actually hurt performance if the endpoint needs to restart frequently
D. They can turn on the Serverless feature for the SQL endpoint.
- ❌ Incorrect - While Serverless can help with scaling, it's not the most direct solution for concurrent queries
- Serverless provides automatic scaling but doesn't specifically address the scaling range limitation
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
- ❌ Incorrect - This is overly complex and not the most direct solution
- Spot instances with reliability optimization is about cost/availability trade-offs, not concurrent query performance
Key Concept:
- Scale up (increase cluster size): For sequential queries, complex individual queries
- Scale out (increase cluster count): For concurrent queries, many users running queries simultaneously
The correct approach is to increase the maximum bound of the scaling range to allow more clusters to handle the concurrent workload.
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