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Answer: Use the Data Flow activity and create a custom mapping between the source and destination columns.
Option B, the Data Flow activity, is the most suitable for handling schema differences in Azure Data Factory. The Data Flow activity allows you to create a custom mapping between the source and destination columns, regardless of the schema differences. By using the 'Derived Column' and 'Source' transformations, you can map the columns from the source dataset to the destination dataset, even if the column names or data types differ. This approach provides a flexible and efficient way to transform data with different schemas.
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You are working on a data transformation project using Azure Data Factory. You have a source dataset with multiple columns, and you need to map these columns to a destination dataset with a different schema. Which of the following Azure Data Factory activities would you use to achieve this, and how would you configure the activity to handle the schema differences?
A
Use the Copy Data activity and configure the 'Allow schema drift' option.
B
Use the Data Flow activity and create a custom mapping between the source and destination columns.
C
Use the Execute SSIS Package activity and import the SSIS package that contains the column mapping logic.
D
Use the Lookup activity to retrieve the destination schema and then map the columns manually.