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DP-203 Derived Column Practice Question

You are designing a data processing solution in Azure Data Factory that uses mapping data flows. You need to perform type conversions on incoming data. Which two transformations can be used to change data types? (Choose two.)

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

✓

Derived Column

Options B and E are correct. Derived Column can change data types through expressions, and Select can cast types during column projection. Option C (Assert) is incorrect because it only validates and routes rows based on conditions; it does not convert data types. Option A (Conditional Split) and Option D (Sort) are also incorrect.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✗

    Conditional Split

    Why it's wrong here

    Conditional Split routes rows to different streams based on expressions and leaves column data types untouched. It is used for branching logic, so it would be correct when rows must be separated by criteria, not when casting a column from string to decimal.

  • ✓

    Derived Column

    Why this is correct

    The Derived Column transformation lets you add or replace columns and explicitly set their data types through the expression builder, so incoming string values can be cast to integer, date or decimal within the mapping data flow.

  • ✗

    Assert

    Why it's wrong here

    Assert evaluates a condition and fails the flow when it is not met; it validates data rather than converting it. It is used to enforce data-quality rules mid-pipeline, so it would be correct when rows must be rejected on a business rule, not when changing column data types.

  • ✗

    Sort

    Why it's wrong here

    Sort orders rows by column values and cannot alter a column's data type. It is used to sequence records before windowed or aggregate operations, so it would be the right transformation when row ordering matters, not when converting string values to integers or timestamps.

  • ✓

    Select

    Why this is correct

    The Select transformation includes a type conversion capability, letting you map each incoming column to a specified data type and rename or drop columns in the same step, satisfying the requirement to change data types within mapping data flows.

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