DP-203 Develop data processing Practice Question
You are building an Azure Data Factory pipeline that processes files from Azure Blob Storage. The pipeline uses a Mapping Data Flow to transform the data and then writes the output to Azure Data Lake Storage Gen2. You need to ensure that the Data Flow can handle schema drift, where incoming files may have additional columns not present in the initial schema. What should you configure in the Data Flow?
⚠ Common exam trap
Watch out — candidates often confuse file path parameters or wildcards with schema drift handling, when schema drift is a specific Data Flow setting.
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 and use 'Auto mapping' in the sink transformation.
In Mapping Data Flow, schema drift is enabled at the source transformation, allowing it to read columns not defined in the projection. The sink must also be configured to write those columns, typically using auto mapping. This combination ensures that additional columns are processed and persisted, which is necessary when incoming files have evolving schemas.
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 and use 'Auto mapping' in the sink transformation.
Why this is correct
Enabling schema drift in the source allows the Data Flow to read columns that are not defined in the projection. Auto mapping in the sink ensures that any new columns are written to the destination. Together, they handle schema drift without manual intervention, which is required for this scenario.
- ✗
Define a fixed schema in the source projection and use a Derived Column transformation to add new columns.
Why it's wrong here
A fixed schema projection will ignore or fail on additional columns, and Derived Column requires you to know the new columns in advance. This does not support dynamic schema drift. The requirement is to handle unknown additional columns, so this approach is incorrect.
- ✗
Use a Parameterized dataset and pass the schema as a parameter at runtime.
Why it's wrong here
Parameterized datasets allow dynamic file paths or table names, not dynamic schema handling. They do not automatically accommodate additional columns in the data. Schema drift must be enabled in the Data Flow itself, so this option does not satisfy the requirement.
- ✗
Set the source dataset to use a wildcard file path and enable 'Recursive' in the source options.
Why it's wrong here
Wildcard file paths and recursive options control file selection, not schema handling. They do not allow additional columns to be processed. Schema drift requires specific settings in the Data Flow transformations, so this approach fails to meet the requirement.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Implement Azure Data Factory Pipelines
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.
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.
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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.