DP-203 Develop data processing Practice Question
You are developing an Azure Data Factory pipeline that processes data from an on-premises SQL Server. The pipeline uses a self-hosted integration runtime. You need to ensure that the pipeline can handle schema changes in the source table without failing. What should you do?
⚠ Common exam trap
Many candidates confuse the schema drift capability of Mapping Data Flows with features of the Copy activity, which does not support automatic schema drift.
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
✓
Use a Mapping Data Flow with 'Allow schema drift' enabled and a sink that supports schema evolution.
Mapping Data Flows with 'Allow schema drift' enabled can dynamically handle schema changes by reading and writing columns that are not predefined. This prevents pipeline failures due to new or removed columns. When paired with a sink that supports schema evolution, such as Delta Lake, the data flow can automatically adapt to changing schemas, ensuring continuous data processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the Copy activity to use a stored procedure that dynamically generates the column list.
Why it's wrong here
Using a stored procedure to generate a column list adds complexity and still requires the Copy activity to have a fixed schema or mapping. It does not natively handle schema drift; if the source schema changes, the stored procedure would need modification. This approach is brittle and not designed for automatic schema evolution.
- ✓
Use a Mapping Data Flow with 'Allow schema drift' enabled and a sink that supports schema evolution.
Why this is correct
Mapping Data Flows support schema drift, allowing the flow to handle new or missing columns at runtime. When enabled, the data flow can process source data with varying schemas without failing. Combined with a sink like Delta Lake or a database that supports schema evolution, this provides a robust solution for schema changes.
- ✗
Enable the 'Allow schema drift' option in the Copy activity source settings.
Why it's wrong here
The 'Allow schema drift' option is available in Mapping Data Flows, not in the Copy activity. Copy activity requires a defined schema or explicit mapping. Enabling it in the source settings is not possible and would not address schema changes. For Copy activity, you must manually update the mapping or use dynamic mapping.
- ✗
Set the 'Auto create table' option in the sink dataset to handle new columns.
Why it's wrong here
The 'Auto create table' option creates a sink table if it does not exist, but it does not alter an existing table to add new columns when the source schema changes. It only applies during initial creation. For ongoing schema changes, you need a mechanism that detects and applies schema updates, such as Mapping Data Flows with schema drift.
Go deeper
Related to this question
Learn chapter
Implement Data Lake Storage
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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