The Databricks Feature Store Client (fs) provides the gettable() method to retrieve a handle to a specific Feature Table. This method returns a Table object, which contains various metadata attributes associated with the table. The description attribute of this Table object holds the string that was provided as the description during the table's creation. Therefore, chaining .description to the fs.gettable("newtable") call directly accesses and returns this metadata string programmatically.

A. This is incorrect. The Feature Store Python API is designed to manage all aspects of a feature table programmatically, including retrieving its metadata.

B. The createtrainingset() method is used to join features from feature tables with a label DataFrame to create a dataset for model training, not to retrieve metadata.

D. The loaddf() method is called on a Table object to load the feature data itself into a Spark DataFrame, not to access its metadata description.

E. This line of code returns the entire Table object, not the specific description string. The question asks for the code that returns the description itself.

1. Databricks Official Documentation

"Manage feature tables": This document provides examples of interacting with feature tables. Under the section "Get a feature table"

it explicitly shows the code featuretable = fs.gettable(tablename) followed by print(featuretable.description) to access the description.

Source: Databricks Machine Learning Guide > Feature Store > Manage feature tables.

2. Databricks Official Documentation

"Feature Store Python API": The API reference details the FeatureStoreClient.gettable method

which returns a Table object. The documentation for the Table class lists description as a public attribute.

Source: Databricks API reference > Machine Learning APIs > Feature Store Python API > databricks.featurestore.client.FeatureStoreClient.gettable and the Table object definition.