This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Pivot
Used to rotate data from rows to columns.
Derived Column
Creates new columns based on expressions.
Union All
Why wrong: Union is the correct transformation; Union All is not a distinct transformation.
Aggregate
Used for grouping and aggregations.
Merge Join
Why wrong: The transformation is called 'Join', not 'Merge Join'.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Pivot
Option A is correct because the Pivot transformation in Mapping Data Flows allows you to rotate data from rows into columns, enabling aggregation and restructuring of datasets. It is a native transformation in Azure Data Factory's Mapping Data Flows, supporting group-by columns, pivot keys, and aggregated column values.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Pivot
Why this is correct
Used to rotate data from rows to columns.
Related concept
Read the scenario before looking for a memorised answer.
Derived Column
Why this is correct
Creates new columns based on expressions.
Related concept
Read the scenario before looking for a memorised answer.
Union All
Why it's wrong here
Union is the correct transformation; Union All is not a distinct transformation.
Aggregate
Why this is correct
Used for grouping and aggregations.
Related concept
Read the scenario before looking for a memorised answer.
Merge Join
Why it's wrong here
The transformation is called 'Join', not 'Merge Join'.
Common exam traps
The trap here is that candidates 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.
Detailed technical explanation
Mapping Data Flows execute on Azure Databricks clusters under the hood, translating transformations into Spark DataFrame operations. The Pivot transformation uses a pivot key column to create new columns, and you must specify an aggregation function (e.g., SUM, COUNT) for the pivoted values; it also requires a group-by column to define row uniqueness. A real-world scenario is converting a sales table with rows per product and month into columns for each month, enabling easier trend analysis.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Pivot — Option A is correct because the Pivot transformation in Mapping Data Flows allows you to rotate data from rows into columns, enabling aggregation and restructuring of datasets. It is a native transformation in Azure Data Factory's Mapping Data Flows, supporting group-by columns, pivot keys, and aggregated column values.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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