DP-203 Develop data processing Practice Question
You are monitoring an Azure Synapse Analytics pipeline that runs daily. The pipeline uses a data flow to transform data. You notice that the data flow is slow and consumes a lot of compute resources. Which action can you take to optimize performance?
⚠ Common exam trap
Test-takers frequently confuse data flow optimization with data loading optimization, leading them to select PolyBase (Option C) or staging tables (Option A) instead of recognizing that partitioning directly addresses compute and parallelism issues in the transformation layer.
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
✓
Partition the data flow by a key column
Partitioning the data flow by a key column (Option D) enables parallel processing across multiple compute nodes, reducing data shuffling and improving throughput. In Azure Synapse data flows, partitioning distributes the workload evenly, which directly addresses the slow performance and high resource consumption observed in the pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a staging table to load data before transformation
Why it's wrong here
Staging tables add an extra step and may not improve data flow performance.
- ✗
Increase the batch size in the data flow
Why it's wrong here
Larger batch size can cause memory pressure.
- ✗
Use PolyBase to load data into the dedicated SQL pool
Why it's wrong here
PolyBase is for loading, not for data flow optimization.
- ✓
Partition the data flow by a key column
Why this is correct
Partitioning allows parallel execution across nodes, improving throughput.
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