DP-203 Develop data processing Practice Question
You are optimizing an Azure Synapse Analytics pipeline that uses mapping data flows. The pipeline runs slowly when processing 100 GB of data. Which THREE settings should you adjust to improve performance?
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
✓
Set 'partition option' to 'Round robin' with a higher number of partitions.
Options A, C, and E are correct. Setting a higher number of partitions with Round robin partitioning improves parallelism across nodes. Increasing Compute type to Memory Optimized and increasing cores provides more resources for data processing. Using Optimize shuffle reduces data movement during joins and aggregations, improving performance. Option B (tumbling window trigger) is a scheduling mechanism, not a performance tuning setting. Option D (Data flow debug) is for development and debugging, not for production performance optimization.
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 'partition option' to 'Round robin' with a higher number of partitions.
Why this is correct
Round robin partitioning distributes data evenly across partitions, improving parallelism.
- ✗
Set a tumbling window trigger to run the pipeline every 5 minutes.
Why it's wrong here
Tumbling window is for scheduling, not for data flow performance tuning.
- ✓
Increase the 'Compute type' to 'Memory Optimized' and increase the number of cores.
Why this is correct
Memory-optimized compute and more cores provide better resource allocation for large data.
- ✗
Enable 'Data flow debug' to monitor execution details.
Why it's wrong here
Debug mode is for development, not for production performance improvement.
- ✓
Use 'Optimize shuffle' in the data flow settings.
Why this is correct
Optimizing shuffle reduces data movement across nodes, improving performance.
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