Question 461 of 760
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your Azure Synapse Analytics pipeline uses PolyBase to load data from Azure Blob Storage into a dedicated SQL pool. The load is slow and suffers from high latency. Which optimization should you apply first?
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
✓
Use a round-robin distribution for the staging table.
Using a round-robin distribution for staging tables minimizes data movement during PolyBase loads, reducing latency. Option A is not the first optimization because splitting files can help parallelism but may not directly address high latency. Option C is not the first optimization because increasing DWU can improve performance but incurs additional cost and is not specific to the load process. Option D is incorrect because clustered columnstore indexes are beneficial for query performance, not for import speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Split the source files into smaller chunks.
Why it's wrong here
Splitting source files can improve parallelism but does not directly address high latency in PolyBase loads.
- ✓
Use a round-robin distribution for the staging table.
Why this is correct
Round-robin distribution avoids data movement during load, which is the primary cause of latency in PolyBase operations.
- ✗
Increase the DWU (Data Warehouse Units) of the SQL pool.
Why it's wrong here
Increasing DWU can improve overall performance but is not the first optimization; it should be considered after distribution optimization.
- ✗
Create clustered columnstore indexes on the staging table.
Why it's wrong here
Clustered columnstore indexes enhance query performance, not load speed.
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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Last reviewed: Jun 21, 2026
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