DP-203 Develop data processing Practice Question
You are designing a data processing pipeline in Azure Data Factory. The pipeline must copy data from Azure Blob Storage to Azure SQL Database and transform the data using a mapping data flow. The data flow includes a Derived Column transformation. What is the purpose of the Derived Column transformation?
⚠ Common exam trap
Candidates often confuse the Derived Column transformation with the Select transformation, assuming it is used for renaming or dropping columns, when in fact Derived Column is specifically for creating or modifying column values via expressions.
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
✓
Create new columns or modify existing columns using expressions.
The Derived Column transformation in Azure Data Factory mapping data flows is used to create new columns or modify existing columns by applying expressions. This allows you to perform calculations, string manipulations, or conditional logic directly within the data flow, enabling in-flight data transformation before writing to the sink.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Aggregate data by grouping rows.
Why it's wrong here
Aggregation is done by Aggregate transformation.
- ✓
Create new columns or modify existing columns using expressions.
Why this is correct
Derived Column allows expression-based column creation and modification.
- ✗
Sort data in ascending or descending order.
Why it's wrong here
Sorting is done by Sort transformation.
- ✗
Rename or drop columns.
Why it's wrong here
Column renaming is done by Select transformation.
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
Introduction to Azure Data Engineering
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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