The correct answer is B) databricks-feature-store. This package is specifically developed to facilitate programmatic interaction with the Databricks Workspace Feature Store. It provides tools for creating and managing feature tables, reading and writing features, retrieving feature metadata, and integrating feature store operations into ML workflows. To install it in a Databricks notebook, use the command: dbutils.library.installPyPI("databricks-feature-store"). Incorrect options include A) databricks-store-engineering (does not exist as a separate entity), C) databricks-workspace-store (not specifically designed for the feature store), and D) databricks-feature-engineering (focuses on feature engineering tasks, not direct feature store interactions). Using the correct client package is crucial for effectively working with the Databricks Workspace Feature Store and ensuring smooth integration with your ML projects.