What is the main function of the augment transformation?

The augment transformation serves the primary purpose of enhancing a dataset by adding new columns from a second dataset based on matching keys. This is particularly useful when there are related datasets that contain additional information that can enrich the primary dataset. For instance, if you have a dataset of customers and a separate dataset with customer demographics, using the augment transformation allows you to enrich your customer dataset by incorporating relevant demographic information.

This method maintains the structure of the original dataset while expanding it with additional columns, thereby creating a more comprehensive and informative dataset. The key aspect of this transformation is that it relies on specific keys to match records between the two datasets, ensuring that the additional data aligns correctly.

In contrast, the other options describe different functionalities that do not align with the primary role of the augment transformation. For example, deleting duplicate records pertains to data cleaning, creating new datasets focuses on amalgamating data in a foundational way rather than enhancing existing data, and combining datasets from multiple sources typically refers to merging, which might involve different algorithms and not just adding columns. Thus, augmenting is explicitly about the targeted addition of columns based on keys, which is why it is the most accurate description of the main function of this transformation.