Databricks Certified Data Engineer - Associate
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A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue is affecting all of their sequentially run queries. They ask the data engineering team for help. The data engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not used by any other user.
Which of the following approaches can the data engineering team use to improve the latency of the data analyst's queries?
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A
They can turn on the Serverless feature for the SQL endpoint.
B
They can increase the maximum bound of the SQL endpoint's scaling range.
C
They can increase the cluster size of the SQL endpoint.
D
They can turn on the Auto Stop 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
Correct Answer: C - They can increase the cluster size of the SQL endpoint.
Why this is correct:
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Problem Analysis: The data analyst is experiencing slow performance on all sequentially run queries using a dedicated SQL endpoint (not shared with other users). This suggests the endpoint lacks sufficient compute resources to handle the query workload efficiently.
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Cluster Size Impact: Increasing the cluster size (using larger instance types or more nodes) provides more compute power, memory, and processing capacity, which directly addresses query latency issues.
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Sequential Query Pattern: Since queries run sequentially (not concurrently), the bottleneck is likely insufficient resources per query rather than concurrency limitations.
Why other options are incorrect:
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A (Turn on Serverless feature): Serverless SQL endpoints automatically scale compute resources, but this doesn't guarantee improved performance for sequentially run queries on a dedicated endpoint. Serverless is more about cost optimization and automatic scaling for variable workloads.
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B (Increase maximum bound of scaling range): This only affects autoscaling limits for concurrent queries. Since queries run sequentially (not concurrently), increasing the maximum scaling range won't help with individual query performance.
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D (Turn on Auto Stop feature): Auto Stop automatically terminates idle clusters to save costs. This feature doesn't improve query performance; it actually might cause cold starts if the endpoint stops and needs to restart.
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E (Serverless + Spot Instance Policy): Similar to option A, Serverless doesn't directly address sequential query performance. Changing Spot Instance Policy to "Reliability Optimized" prioritizes availability over cost savings but doesn't improve query latency for sequential workloads.
Key Takeaway: For dedicated SQL endpoints with sequential query patterns experiencing latency issues, increasing cluster size (using more powerful instances) is the most direct solution to improve query performance.
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