Question

A data scientist creates a feature table in Unity Catalog containing customer aggregates keyed by customer_id. They train a model using a FeatureLookup so that the training set joins these features automatically. When the model is later deployed for batch inference, the inference DataFrame only contains customer_id and a few raw request columns. Which behavior should the data scientist expect when using score_batch (or the equivalent Feature Engineering scoring API) with this logged model?

Accepted Answer

The scoring API automatically retrieves the required features from the feature table using customer_id, so the inference DataFrame does not need to include the looked-up feature columns.

Answer

The inference DataFrame must contain all feature columns because the model does not store any information about the feature lookups used during training.

Answer

The features must be manually joined to the inference DataFrame before scoring, since feature metadata is discarded once the model is logged.

Answer

Batch inference will fail unless the feature table is first exported to an online store, because offline feature tables cannot be used at scoring time.