Spark jobs take too long to start in a workspace. Which settings do you review?
Why C is the answer
Spark workspace settings, such as the starter pool
Spark workspace settings control which pool and defaults sessions use, and starter pools are built for fast session starts.
In detail
- Spark workspace settings, such as the starter pool, affect start time.
- Custom pools suit jobs with special size or library needs.
Use case
An illustrative scenario, not a real incident.
Analysts wait for a session each time they open a notebook. Reviewing the workspace Spark settings shows that a starter pool would give quicker starts.
See it in different ways
mindmap
root((Monitoring))
Activities
Monitor hub
Capacity
Capacity Metrics app
Warehouse
Query insights
Spark
Logs
Copy step
Run detailsflowchart LR S0["Slow session start"] --> S1["Open Spark settings"] --> S2["Review the pool"] --> S3["Test again"]
Key point: Workspace Spark settings shape session start.
Why the other options are wrong
- A. The sensitivity label on each notebook
- Labels have no effect on start time.
- B. The OneLake shortcut targets
- Shortcut targets have no effect on start time.
- D. The semantic model refresh schedule
- That refresh is unrelated to Spark sessions.