DP-203 Develop data processing Practice Question
You are designing a data processing pipeline in Azure Data Factory that uses a Mapping Data Flow. You need to handle errors gracefully, such as when a row fails to convert a column value. Which TWO actions should you take? (Choose two.)
⚠ Common exam trap
It's easy for candidates to confuse pipeline-level error handling (like Try-Catch) with data flow-level error handling, or they assume schema drift can fix data type mismatches, when in fact it only handles structural changes at the source.
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
✓
Configure the sink transformation to allow errors and log error rows to a separate file.
Configuring the sink transformation to allow errors and log error rows to a separate file enables graceful error handling in Mapping Data Flows. This approach captures rows that fail during transformation (e.g., type conversion errors) and writes them to a designated error output, allowing the pipeline to continue processing valid rows. Option E is correct because a Conditional Split transformation can proactively identify rows that are likely to cause errors based on a condition (e.g., checking for null or invalid data types) and route them to a separate path for logging or remediation, preventing them from reaching the sink and causing failures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Wrap the data flow in a Try-Catch activity in the pipeline.
Why it's wrong here
Mapping Data Flows do not have try-catch; you handle errors within the data flow.
- ✗
Set the data flow's error handling to 'Abort on error' to stop processing on first failure.
Why it's wrong here
Aborting is not graceful; it stops the entire pipeline.
- ✗
Enable schema drift on the source to automatically handle data type mismatches.
Why it's wrong here
Schema drift handles new columns, not conversion errors.
- ✓
Configure the sink transformation to allow errors and log error rows to a separate file.
Why this is correct
Sink can be configured to continue on error and write error rows to a file.
- ✓
Use a Conditional Split transformation to separate rows that cause errors based on a condition.
Why this is correct
Conditional split allows routing error rows to a separate sink for logging.
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