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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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:
✅ Why B: Increase the maximum bound of the SQL endpoint’s scaling range is correct
The problem describes many users running many small queries at the same time on the same always-on SQL endpoint.
This is a concurrency problem, not a single-query performance problem.
- Databricks SQL endpoints scale horizontally, not just vertically.
- Increasing the maximum scaling bound allows the endpoint to add more clusters.
- More clusters = more parallel query execution, which directly reduces latency for concurrent small queries.
This matches the scenario perfectly.
❌ Why A: Increase the cluster size is not ideal
- Increasing cluster size scales vertically (bigger machines).
- This helps complex or heavy queries, not many small concurrent queries.
- A single larger cluster can still become a bottleneck under high concurrency.
Exam takeaway 🧠
- Concurrency issue → scale out (B)
- Heavy/complex queries → scale up (A)
This distinction is critical for the Databricks Data Engineer Associate exam.
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