DP-203 Develop data processing Practice Question
Which TWO options are correct approaches to handle schema drift in Azure Data Factory Mapping Data Flows?
⚠ Common exam trap
Many candidates confuse handling schema drift with data routing or error handling, and they overlook that enabling schema drift is the foundational step that must be taken before any other transformations can work with the drifted columns.
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 is the primary mechanism in Mapping Data Flows to handle incoming columns that are not defined in the dataset schema. This setting allows the data flow to dynamically adapt to changes in the source data structure, such as new or missing columns, without requiring manual schema updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a conditional split to route rows with different schemas to separate sinks.
Why it's wrong here
This does not handle drift in the main flow; it complicates processing.
- ✗
Define a rigid schema in the source dataset and reject rows that don't match.
Why it's wrong here
This would fail on schema changes, not handle drift.
- ✗
Disable schema drift to improve performance.
Why it's wrong here
Disabling drift will cause failures on schema changes.
- ✓
Enable 'Allow schema drift' in the source transformation.
Why this is correct
This allows the data flow to handle changing columns.
- ✓
Use a derived column transformation to provide default values for missing columns.
Why this is correct
This ensures downstream transformations have expected columns.
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