DP-203 Develop data processing Practice Question
You are designing an ETL process in Azure Data Factory. You need to transform data using Mapping Data Flows. Which THREE of the following transformations are available in Mapping Data Flows?
⚠ Common exam trap
Test-takers frequently confuse the 'Union All' and 'Merge Join' names from other tools (like SSIS or T-SQL) with the actual transformation names in Azure Data Factory Mapping Data Flows, leading them to select options that sound familiar but are not available.
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
✓
Pivot
In Azure Data Factory Mapping Data Flows, the Pivot transformation (A) is a built-in transformation that reshapes data by turning unique row values into columns, which is why it is correct. The Derived Column transformation (B) is also available and is used to create new columns or modify existing ones using expression builder logic, making it correct. The Aggregate transformation (D) is likewise a native Mapping Data Flows transformation that performs group-by operations and aggregations such as SUM, AVG, COUNT, and MIN/MAX, so it is correct. Union All (C) is not a Mapping Data Flows transformation; combining multiple streams is done with the Union transformation, which behaves like a union (not specifically 'Union All' as a named transformation). Merge Join (E) is not a Mapping Data Flows transformation either; joins are performed with the Join transformation, and the term 'Merge Join' refers to a SQL Server/SSIS-style operator rather than an ADF Mapping Data Flows transformation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pivot
Why this is correct
Pivot is a native Mapping Data Flows transformation, reshaping rows into columns through an aggregate-style pivot operation on selected group-by and pivot keys. It satisfies the stem's requirement for transformations available within Mapping Data Flows, unlike pipeline activities or external compute, running on the Spark execution engine.
- ✓
Derived Column
Why this is correct
Derived Column is a native Mapping Data Flows transformation, letting you generate new columns or modify existing ones using expression-builder logic during the data flow execution. It satisfies the stem's requirement for transformations available within Mapping Data Flows, unlike pipeline activities such as Copy or Lookup, which operate outside the data flow canvas.
- ✗
Union All
Why it's wrong here
Union is the correct transformation; Union All is not a distinct transformation.
- ✓
Aggregate
Why this is correct
Aggregate is a native Mapping Data Flow transformation, performing group-by aggregations such as SUM, COUNT, and AVG on streamed data within the data flow graph. It satisfies the requirement by providing in-pipeline aggregation without invoking external compute.
- ✗
Merge Join
Why it's wrong here
Merge Join is not a Mapping Data Flows transformation; the available join transformation is simply named Join, offering inner, left outer, right outer, full outer and cross types. Merge Join belongs to SSIS, making it tempting for engineers migrating packages, but it is not selectable in data flows.
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