DP-203 Develop data processing Practice Question
You are designing an Azure Synapse Analytics pipeline that uses a Mapping Data Flow to transform data from Azure Data Lake Storage Gen2. The data flow must handle schema drift, where new columns can appear in the source files over time. You need to ensure that the new columns are automatically included in the sink output without modifying the data flow. What should you do?
⚠ Common exam trap
The trap here is enabling schema drift only on the source or only on the sink, while both are required for new columns to flow through automatically.
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 on the source and sink, and use the sink's automatic mapping.
Schema drift in Mapping Data Flows requires enabling Allow schema drift on both the source and the sink. The source reads new columns at runtime, and the sink's automatic mapping writes them to the destination without predefined column mappings. Using derived columns, Select, or Flatten transformations does not automatically propagate unknown columns and would require manual changes when the schema evolves.
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 on the source transformation and use a derived column to map new columns.
Why it's wrong here
Enabling Allow schema drift on the source is necessary, but a derived column cannot dynamically map unknown new columns because derived columns require explicit column definitions. It would not automatically include new columns in the sink output. You would need to manually update the derived column for each new column, which violates the requirement to avoid modifying the data flow.
- ✗
Use a Select transformation to rename columns and enable schema drift on the sink only.
Why it's wrong here
A Select transformation requires explicit column mappings and does not dynamically handle new columns. Enabling schema drift only on the sink is insufficient because the source must also be configured to allow drift so that new columns are read into the data flow. This option would not automatically include new source columns and would require manual updates.
- ✓
Enable Allow schema drift on the source and sink, and use the sink's automatic mapping.
Why this is correct
Enabling Allow schema drift on both the source and sink allows the data flow to read new columns from the source and write them to the sink without explicit mapping. The sink's automatic mapping picks up the drifted columns at runtime. This satisfies the requirement to include new columns automatically without editing the data flow when the source schema changes.
- ✗
Use a Flatten transformation and enable schema drift on the source.
Why it's wrong here
A Flatten transformation is used to unroll hierarchical or nested structures, not to handle schema drift for new top-level columns. Enabling schema drift on the source alone does not ensure the sink writes the new columns; the sink must also allow drift. This option does not meet the requirement and the Flatten transformation is irrelevant to the scenario.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.