Question

A data scientist is using a Databricks Feature Engineering client to build a training dataset. They call fe.create_training_set() with a DataFrame containing a 'customer_id' column and several FeatureLookup objects that pull columns from a feature table keyed on 'customer_id'. When they later call fe.log_model() and score new data with fe.score_batch(), they want the model to automatically retrieve the looked-up features at inference time using only the raw input containing 'customer_id'. Which behavior of the returned TrainingSet supports this workflow?

Accepted Answer

The TrainingSet packages the feature lookup metadata so that a model logged with fe.log_model() records the feature table dependencies and can rehydrate features automatically during fe.score_batch().

Answer

The TrainingSet materializes the joined features into a new permanent Unity Catalog table that fe.score_batch() must be pointed to explicitly at inference time.

Answer

The TrainingSet requires the data scientist to manually re-join the feature table during scoring because lookup metadata is not persisted with the model.

Answer

The TrainingSet strips the 'customer_id' lookup key from the training DataFrame, so it must be re-added by hand before calling fe.score_batch().