Question

A data scientist wants to create a new feature table in Unity Catalog from a Spark DataFrame that contains one row per customer, keyed by 'customer_id'. They plan to use these features later for training and batch scoring via feature lookups. Which approach correctly creates the feature table?

Accepted Answer

Use FeatureEngineeringClient.create_table(), specifying the table name, primary_keys='customer_id', and the DataFrame via the df argument

Answer

Save the DataFrame with df.write.saveAsTable() and then run ALTER TABLE to designate it as a feature table

Answer

Call mlflow.log_table() with the DataFrame and pass primary_keys='customer_id' as an artifact parameter

Answer

Use FeatureEngineeringClient.write_table() without defining any primary key, since Unity Catalog infers keys automatically