DP-203 Develop data processing Practice Question
Your company uses Azure Synapse Analytics and has deployed a pipeline that uses a Mapping Data Flow to transform data. The data flow reads from a source in Azure Blob Storage and writes to a dedicated SQL pool. You notice that the data flow is running slowly and consuming a lot of Data Flow cluster resources. You need to improve performance without increasing the cluster size. Which action should you take?
⚠ Common exam trap
A common mix-up: candidates confuse increasing batch size (Option B) with improving parallelism, but batch size only affects sink write operations, not the internal data processing distribution that causes cluster resource exhaustion.
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
✓
Add a partitioning step in the data flow to distribute the data across partitions based on a key column.
Adding a partitioning step in the Mapping Data Flow distributes data across partitions based on a key column, which allows parallel processing across the cluster's nodes. This reduces data shuffling and improves throughput without increasing the cluster size, directly addressing the performance bottleneck caused by skewed or unpartitioned data.
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 self-hosted integration runtime instead of the default auto-resolve IR.
Why it's wrong here
The IR type does not directly affect data flow performance; it affects connectivity.
- ✗
Increase the batch size in the data flow settings to reduce the number of round trips.
Why it's wrong here
Increasing batch size may cause memory pressure and slow down processing.
- ✓
Add a partitioning step in the data flow to distribute the data across partitions based on a key column.
Why this is correct
Partitioning the data can improve parallelism and performance.
- ✗
Change the source format to Delta Lake to leverage optimizations.
Why it's wrong here
Delta Lake is not directly supported as a source in Mapping Data Flows; it requires a different approach.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Senior Network & Security Engineer · founder of Courseiva
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