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Answer: Use the Data Flow activity and configure the 'Conditional Split' transformation to handle different conditions.
Option B, the Data Flow activity, is the most suitable for implementing conditional transformations in Azure Data Factory. By using the 'Conditional Split' transformation within the Data Flow, you can define different conditions and route the data to different transformations or destinations based on those conditions. This approach allows for flexible and efficient conditional data processing without the need for custom activities or external packages.
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You are working on a data transformation project using Azure Data Factory and need to perform a series of conditional transformations on a dataset. Which of the following Azure Data Factory transformations would you use to implement these conditional transformations, and how would you configure the transformation to handle different conditions?
A
Use the Copy Data activity and configure the 'Mapping' tab to define conditional mappings based on the data values.
B
Use the Data Flow activity and configure the 'Conditional Split' transformation to handle different conditions.
C
Use the Execute SSIS Package activity and import the SSIS package that contains the conditional logic.
D
Use the Lookup activity to retrieve the conditions and then use a custom activity to perform the conditional transformations.
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