A destination data platform that can execute transformations after data is loaded is the correct option.

This statement describes the ELT pattern where data is extracted and loaded first and then transformed inside the destination system. The destination must be able to run transformations such as SQL queries, joins, aggregations, user defined functions, or stored procedures at scale for an extract load transform implementation to work correctly.

Dataflow is incorrect because it is a stream and batch processing service that can perform transformations during movement but it is not a required capability for the ELT pattern. The ELT approach depends on the destination being able to transform data after loading rather than on a particular processing tool.

Source data that is completely transformed before loading into the destination is incorrect because that describes a traditional ETL workflow where transformations happen before load. An extract load transform implementation specifically relies on doing transformations after the data is loaded into the destination.

An orchestration workflow that moves data and invokes transformations in separate tools is incorrect because orchestration can coordinate steps but it is not the essential capability that makes ELT possible. The key requirement is that the destination itself can execute the transformations once the data is loaded.

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