Which decorator should be used for Transforms that handle file-based datasets instead of DataFrame objects?

The correct answer is the decorator that specifically applies to file-based datasets instead of DataFrame objects. In the context of data engineering, particularly within frameworks like Palantir Foundry, the choice of decorators is crucial for specifying how data transformations should be applied.

The decorator designed for transforms that handle file-based datasets is meant to ensure that the input and output of the transformation respect the structure and characteristics of files, which can include various formats such as CSV, TXT, or other file types. This distinction is important because file-based datasets have different access patterns, performance implications, and data handling methodologies compared to in-memory DataFrame objects.

Using a decorator such as @transform indicates a more general approach that can apply to various types of data structures, including both DataFrames and file-based datasets. This flexibility means that it can seamlessly handle different data formats within the same transformation workflow.

In contrast, the other options likely refer to more specialized decorators that are not primarily intended for file-based transformations or may not even exist in this context. Therefore, choosing the correct decorator is essential for ensuring that the transformation logic is appropriate for the dataset type being handled.