You have an Azure Data Factory pipeline that executes a stored procedure in Azure SQL Database. The pipeline fails with an error indicating that the stored procedure ran out of memory. What change should you make to the pipeline to resolve this?
Higher service tiers provide more memory for the database.
Why this answer
The error indicates that the stored procedure ran out of memory, which is a resource limitation at the database level, not a transient failure or timeout issue. Scaling up the Azure SQL Database to a higher service tier (e.g., from Standard to Premium or increasing DTU/vCore count) provides more memory and compute resources, directly resolving the out-of-memory condition.
Exam trap
The trap here is that candidates confuse pipeline-level retries or timeouts with database-level resource constraints, assuming that retrying or waiting longer will fix a memory exhaustion error, which is a hard resource limit that requires scaling the database.
How to eliminate wrong answers
Option A is wrong because a retry policy only re-executes the activity on transient failures (e.g., network blips), but an out-of-memory error is a persistent resource constraint that will recur on retry. Option B is wrong because increasing the pipeline activity timeout extends the duration the pipeline waits for completion, but does not address the underlying memory shortage in the database. Option C is wrong because using a Self-Hosted Integration Runtime shifts data movement or activity execution to an on-premises or VM-based runtime, but does not affect the memory allocation of the Azure SQL Database where the stored procedure runs.