Databricks Data Engineer Professional · Free practice question 4 of 12
SQL warehouse sizing versus scaling
On a Databricks SQL warehouse at Elmstead Finance, a single complex month-end query runs slowly while nothing else is queued, and dozens of short dashboard queries on another warehouse wait in a queue at 9 a.m. each day. Which changes address each problem?
- A.Increase the maximum cluster count for the month-end warehouse and the cluster size for the dashboard warehouse
- B.Enable auto stop on both warehouses
- C.Move both workloads to a single all-purpose cluster
- D.Increase the cluster size for the month-end warehouse and the maximum cluster count (scaling) for the dashboard warehouse
Show answer and explanation
Correct answer: D. Increase the cluster size for the month-end warehouse and the maximum cluster count (scaling) for the dashboard warehouse
Why: A larger warehouse size adds compute to each cluster, which helps an individual complex query, while raising the maximum number of clusters lets the warehouse scale out to serve many concurrent queries and shorten queues. Swapping them gives the dashboards bigger clusters that still queue and gives the month-end query more clusters it cannot use. Auto stop saves cost when idle but does not improve performance.
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