DP-203 Develop data processing Practice Question
You are using Azure Data Factory to copy data from an Azure SQL Database to an Azure Data Lake Storage Gen2 account. The copy activity is failing intermittently with a timeout error. You need to improve the throughput and reliability of the copy operation. What should you do?
⚠ Common exam trap
The trap here is assuming that fault tolerance or larger batch sizes solve timeouts, when the real fix is increasing parallelism and using staging.
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 degree of copy parallelism and enable staged copy.
Intermittent timeouts often occur when a single connection cannot sustain the load. Increasing the degree of copy parallelism opens more concurrent connections, distributing the load. Staged copy separates the read and write phases, reducing the chance of timeouts during the write to ADLS Gen2. Together, these settings improve both throughput and reliability for large-scale copies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the degree of copy parallelism and enable staged copy.
Why this is correct
Increasing the degree of copy parallelism allows multiple concurrent connections to the source, improving throughput. Enabling staged copy uses a temporary staging area in Blob Storage or ADLS Gen2 to decouple extraction and loading, which can improve reliability and performance for large datasets. This combination addresses both throughput and intermittent timeouts.
- ✗
Configure the copy activity to use a single thread with a larger batch size.
Why it's wrong here
A single thread limits concurrency and is unlikely to improve throughput. A larger batch size may increase memory usage and does not prevent timeouts. This configuration reduces parallelism, which is the opposite of what is needed to improve performance for a large copy operation.
- ✗
Set the copy activity's fault tolerance to skip incompatible rows.
Why it's wrong here
Fault tolerance settings control how errors like incompatible rows are handled, but they do not improve throughput or prevent timeouts. Skipping rows may hide data issues. This setting is for error handling, not performance or reliability of the connection.
- ✗
Change the source dataset to use a stored procedure that returns all rows at once.
Why it's wrong here
Using a stored procedure that returns all rows can increase memory pressure and does not improve parallelism. It may worsen timeouts because the entire result set must be materialized. This approach does not address the underlying throughput issue and may introduce additional latency.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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