DP-203 Develop data processing Practice Question
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?
⚠ Common exam trap
Candidates often 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.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Scale up the Azure SQL Database to a higher service tier.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a retry policy to the stored procedure activity.
Why it's wrong here
Retry does not resolve resource exhaustion.
- ✗
Increase the pipeline activity timeout.
Why it's wrong here
Timeout does not increase memory allocation.
- ✗
Use a Self-Hosted Integration Runtime instead of Azure IR.
Why it's wrong here
IR is for connectivity, not database memory.
- ✓
Scale up the Azure SQL Database to a higher service tier.
Why this is correct
Higher service tiers provide more memory for the database.
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Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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