Databricks Certified Data Engineer - Associate

Explanation

When many team members are running small queries simultaneously on the same SQL endpoint, the issue is likely concurrency - the endpoint doesn't have enough resources to handle all the concurrent queries efficiently.

Let's analyze each option:

A. They can increase the cluster size of the SQL endpoint.

• This would increase the compute power of a single cluster, but doesn't directly address concurrency issues with many small simultaneous queries.
• Larger cluster size helps with complex, resource-intensive queries, not necessarily many small concurrent queries.

B. They can increase the maximum bound of the SQL endpoint’s scaling range. ✓

• This is the correct answer. SQL endpoints in Databricks can scale automatically based on workload.
• By increasing the maximum bound of the scaling range, you allow the endpoint to spin up more clusters to handle concurrent queries.
• When many small queries run simultaneously, the endpoint can distribute them across multiple clusters, improving latency.

C. They can turn on the Auto Stop feature for the SQL endpoint.

• Auto Stop automatically stops clusters after a period of inactivity to save costs.
• This doesn't help with performance during active query execution; it's a cost-saving feature.

D. They can turn on the Serverless feature for the SQL endpoint.

• Serverless SQL endpoints automatically scale compute resources, but the question describes an "always-on SQL endpoint" which suggests it's already provisioned.
• While Serverless can help with scaling, the most direct solution for an existing endpoint with scaling limits is to increase the maximum bound.

Key Concept: SQL endpoints have scaling ranges (minimum and maximum cluster counts). When many users run queries simultaneously, increasing the maximum bound allows the endpoint to create more clusters to handle the concurrent workload, reducing latency through better resource distribution.