DP-203 Develop data processing Practice Question
Which TWO techniques can you use to handle schema drift in Azure Data Factory mapping data flows?
⚠ Common exam trap
Watch out — candidates often confuse 'handling schema drift' with 'ignoring or rejecting unknown columns' (options D and E), or they think manual column-by-column handling (option B) is a valid technique, when in fact ADF provides automated drift handling through the source setting and pattern matching.
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
✓
Enable 'Allow schema drift' in the source transformation
Enabling 'Allow schema drift' in the source transformation tells Azure Data Factory (ADF) mapping data flows to dynamically accept incoming columns that are not defined in the schema at design time. This is the primary built-in mechanism for handling schema drift without manual intervention, as it automatically propagates new columns through the data flow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable 'Allow schema drift' in the source transformation
Why this is correct
Allows columns to be added without breaking the pipeline.
- ✗
Use derived column transformation to handle each new column manually
Why it's wrong here
Manual approach does not scale for drift.
- ✓
Use column pattern matching to automatically map columns with similar names
Why this is correct
Patterns like 'col_*' can map dynamic columns.
- ✗
Use assertion rules to reject rows with unknown columns
Why it's wrong here
Rejection is not handling drift; it fails the pipeline.
- ✗
Use a fixed schema mapping to ignore unknown columns
Why it's wrong here
Fixed mapping would cause errors on new columns.
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