What parameter does the feature store Table creation require for primary keys?

The requirement for primary keys in feature store table creation emphasizes the need for unique identifiers for each record. Having unique identifiers is essential because they ensure that each entry in the feature store can be precisely identified and retrieved. This uniqueness is crucial in avoiding conflicts and ambiguity when accessing data, especially in training machine learning models where the integrity and distinctness of data points can significantly affect the model's performance.

Unique identifiers also facilitate the merging and retrieval processes when features are joined across different datasets, maintaining the referential integrity of the data. This becomes particularly important during model training when features from various tables may come together.

In considering the other options, composite keys might not always meet the requirement for uniqueness if not carefully structured; feature names being non-null does not guarantee they are unique; and using any random string fails to ensure uniqueness or practical identification of records. Thus, the emphasis on unique identifiers stands out as the correct approach for establishing primary keys in feature store tables.