Databricks Machine Learning Associate Question 71
Select 3You are working on a machine learning pipeline to predict customer churn for a subscription-based service. Your team decides to use the Databricks Feature Store to manage the features used in the pipeline. Which of the following are benefits of using the Feature Store in this scenario?
- A
Ensures feature consistency between training and inference phases.
- B
Automatically tunes hyperparameters for the machine learning model.
- C
Facilitates feature reuse across multiple machine learning projects.
- D
Enables tracking of feature lineage and versioning.
- E
Reduces the need for data pre-processing in model training.
Show answer and explanation
Correct answers: A, C, D
Explanation
The Databricks Feature Store provides several key benefits, including ensuring consistency of features between training and inference, enabling feature reuse across projects, and managing feature lineage and versioning. These capabilities streamline the machine learning workflow and improve model reliability. However, it does not handle hyperparameter tuning or eliminate the need for pre-processing raw data.
- A. Correct.
Ensuring feature consistency between training and inference is a core benefit of using the Feature Store, as it avoids discrepancies in feature computation.
- B. Incorrect.
Hyperparameter tuning is not a functionality of the Feature Store; it is typically handled by model training frameworks or libraries.
- C. Correct.
The Feature Store allows features to be registered and reused across different projects, preventing redundant work and promoting collaboration.
- D. Correct.
Feature lineage and versioning are key features of Databricks Feature Store, enabling users to understand and manage changes to features over time.
- E. Incorrect.
While the Feature Store helps organize and manage features, it does not eliminate the need for data pre-processing before model training.