DP-203 Develop data processing Practice Question
You are developing a data processing pipeline in Azure Data Factory. The pipeline uses a mapping data flow to transform data. You need to ensure that the data flow can handle schema drift from the source. Which THREE of the following actions should you take? (Select THREE.)
⚠ Common exam trap
Many candidates think they must explicitly define every column or use static transformations to handle schema drift, when in fact Azure Data Factory's mapping data flow provides dynamic mechanisms like 'Allow schema drift', column patterns, and drift type handling to automatically accommodate unknown 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 settings.
Enabling 'Allow schema drift' in the source settings of a mapping data flow tells Azure Data Factory to accept incoming columns that are not defined in the source schema. This is essential for handling schema drift, as it prevents the pipeline from failing when new columns appear in the source data.
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 'Select' transformation to explicitly choose only the known columns.
Why it's wrong here
This would drop drifted columns.
- ✓
Enable 'Allow schema drift' in the source settings.
Why this is correct
This allows the data flow to accept additional columns.
- ✓
Set 'Ignore drift column types' in the sink settings to avoid type mismatch errors.
Why this is correct
This prevents failures when drifted columns have different data types.
- ✓
Use 'Column pattern' transformations to apply transformations to drifted columns.
Why this is correct
Column patterns allow dynamic handling of unknown columns.
- ✗
Add a 'Derived Column' transformation for every possible column that might be added.
Why it's wrong here
This is not scalable and does not handle unknown columns.
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