DP-203 Develop data processing Practice Question
You are running a pipeline in Azure Data Factory that uses a Mapping Data Flow. The data flow reads from Azure SQL Database and writes to Azure Synapse Analytics. You find that the data flow is very slow. Which configuration change would most likely improve performance?
⚠ Common exam trap
A common mix-up: candidates confuse Mapping Data Flow performance tuning with Copy Activity optimizations, such as PolyBase or staging, which are irrelevant to Spark-based data flows.
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
✓
Increase the 'Compute type' to 'Memory Optimized' and the 'Core count'
Mapping Data Flows in Azure Data Factory execute on Spark clusters. The default compute configuration may not provide sufficient memory or parallelism for large data volumes. Increasing the 'Compute type' to 'Memory Optimized' and raising the 'Core count' directly allocates more memory and processing cores to the Spark cluster, which accelerates transformations and data movement between Azure SQL Database and Azure Synapse Analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the 'Staging' option to 'Use staging'
Why it's wrong here
Staging is not applicable to data flows.
- ✓
Increase the 'Compute type' to 'Memory Optimized' and the 'Core count'
Why this is correct
More compute resources speed up data flow execution.
- ✗
Enable staging for the sink and use PolyBase
Why it's wrong here
This is a valid optimization for copy activities, not data flows.
- ✗
Set the 'Partition option' to 'Round robin' on the source
Why it's wrong here
Partitioning may help, but compute resources are the primary factor.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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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