What is the primary function of a slice transformation?

The primary function of a slice transformation is to remove fields from a dataset, which means it is used to select and retain only the specific fields that are relevant to the analysis. This transformation helps in refining the dataset to focus on the necessary data attributes, reducing complexity, and improving performance by eliminating unnecessary information.

By retaining only the fields of interest, analysts can streamline their data processing and make it easier to visualize and interpret the data without the distraction of irrelevant columns. This process is particularly useful in data preparation stages, where creating a clean and targeted dataset is crucial for accurate analysis and insights.

Regarding the other options, changing row values pertains more to a transformation that alters existing data rather than removing it, calculating metrics involves summarizing or aggregating data rather than selective removal, and filtering entire datasets typically means excluding rows based on certain conditions rather than the removal of specific columns or fields. Each of these functions serves different purposes, but the slice transformation specifically focuses on field selection.