Databricks Machine Learning Associate Question 74
Select 3A data science team is building a machine learning pipeline for a real-time recommendation system. They want to ensure features are reused across multiple models and consistently updated to avoid feature drift. What are the benefits of using Databricks Feature Store in this scenario?
- A
It enables feature sharing and reuse across multiple models.
- B
It ensures consistent feature computation and prevents feature drift.
- C
It automatically tunes hyperparameters for machine learning models.
- D
It provides metadata tracking and lineage for features.
- E
It eliminates the need for offline storage of feature data.
Show answer and explanation
Correct answers: A, B, D
Explanation
Databricks Feature Store is a centralized repository for storing and managing features used in machine learning pipelines. It ensures consistent feature computation, supports feature reuse across models, and provides metadata tracking and lineage for features. These capabilities help teams improve productivity, ensure consistency between training and inference, and maintain better traceability of features.
- A. Correct.
Correct. Databricks Feature Store allows features to be shared and reused across multiple models, improving efficiency and consistency.
- B. Correct.
Correct. Databricks Feature Store helps maintain consistency in feature values across training and inference, reducing the risk of feature drift.
- C. Incorrect.
Incorrect. Hyperparameter tuning is not a functionality provided by Databricks Feature Store; it focuses on feature storage and management.
- D. Correct.
Correct. Metadata tracking and lineage for features are core functionalities of Databricks Feature Store, enabling better traceability and management.
- E. Incorrect.
Incorrect. Databricks Feature Store does not eliminate the need for offline storage; it integrates with both offline and online storage systems.