DP-203 Design and implement data storage Practice Question
You are troubleshooting slow COPY INTO performance in Azure Synapse Analytics dedicated SQL pool when loading Parquet files from Azure Data Lake Storage Gen2. The files are 1 GB each. What should you do to improve performance?
⚠ Common exam trap
The trap here is that candidates focus on file size reduction (Option A) as a general optimization, but the specific requirement in Synapse dedicated SQL pool is to match the number of files to the number of distributions (60) to avoid distribution skew and maximize parallelism.
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 number of files to match the number of distributions
COPY INTO in Azure Synapse dedicated SQL pool distributes data across 60 distributions. To maximize parallelism, the number of input files should match or exceed the number of distributions. With 1 GB files, you have too few files to fully utilize all distributions, causing some distributions to remain idle. Increasing the number of files to at least 60 ensures each distribution gets work, improving throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the file size to 100 MB to increase parallelism
Why it's wrong here
Reducing file size to 100 MB does not directly address the parallelism issue; the problem is too few files to match the 60 distributions, not the file size.
- ✗
Use PolyBase instead of COPY INTO
Why it's wrong here
PolyBase is an older technology; COPY INTO is the recommended and more performant approach for loading Parquet files in Synapse dedicated SQL pool.
- ✓
Increase the number of files to match the number of distributions
Why this is correct
Correct. With 1 GB files, there are too few files to fully utilize all 60 distributions. Increasing the number of files to at least 60 ensures each distribution receives data, maximizing parallelism and improving performance.
- ✗
Disable parallel processing in the COPY command
Why it's wrong here
Disabling parallel processing would reduce performance by forcing sequential execution, which is counterproductive for large data loads.
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