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
Grouping and aggregating rows is performed by the Aggregate transformation, which supports group-by and functions such as sum, count and average. Derived Column instead evaluates expressions to add or modify individual column values without collapsing rows. Aggregate would be correct when the data flow must compute totals or counts per category.
- ✓
Create new columns or modify existing columns using expressions.
Why this is correct
The Derived Column transformation generates new columns or overwrites existing ones by evaluating expression-language formulas against incoming stream data, satisfying the pipeline's requirement to transform data within the mapping data flow. Unlike Select, which only renames or drops columns, it computes values, enabling calculated fields during the Blob Storage to Azure SQL Database copy.
- ✗
Sort data in ascending or descending order.
Why it's wrong here
Derived Column creates new or modified columns using expression-based calculations, not ordering. Sorting is handled by the Sort transformation, which arranges rows by specified columns. Sorting is tempting because it is a common data-flow operation, but the Derived Column transformation's purpose is column computation.
- ✗
Rename or drop columns.
Why it's wrong here
Renaming and dropping columns is handled by the Select transformation, which manages column projection and metadata. The Derived Column transformation creates new columns or modifies existing ones using expressions, so it cannot remove or rename schema fields. Select would be correct when the pipeline needs to prune or relabel columns before loading.
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
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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Senior Network & Security Engineer · founder of Courseiva
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