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Answer: Configure the session timeout to be 25 minutes.
The 15-minute delay is caused by Spark session initialization overhead. Since sessions run every 40 minutes for 15 minutes each, there's a 25-minute gap between sessions. By configuring the session timeout to 25 minutes (option A), the Spark session remains active between runs, eliminating the initialization delay. This keeps the session 'warm' and ready for immediate execution. Option B (increasing nodes) doesn't address initialization delays. Option C (dynamic executors) is for resource optimization, not session startup. Option D (isolated compute) provides security isolation but doesn't reduce startup time.
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You have an Azure Machine Learning workspace with a serverless Spark compute resource. You run the same Spark session every 40 minutes, and each session lasts for 15 minutes. During testing, you notice a 15-minute delay at the beginning of each Spark session. You need to reduce this initial delay to under one minute. What should you do?
A
Configure the session timeout to be 25 minutes.
B
Increase the number of nodes to 16.
C
Enable dynamically allocated executors.
D
Enable an isolated compute.