Databricks Machine Learning Associate Question 75
Select 3You are designing a machine learning pipeline for a recommendation system and need to reuse features across multiple models while ensuring consistency and avoiding duplication. Why should you consider using the Databricks Feature Store in this scenario?
- A
It ensures feature consistency across training and serving environments.
- B
It automatically tunes hyperparameters for machine learning models.
- C
It simplifies feature reuse across multiple models or teams.
- D
It eliminates the need for tracking data lineage of features.
- E
It allows you to store and serve features with low-latency APIs.
Show answer and explanation
Correct answers: A, C, E
Explanation
The Databricks Feature Store offers several key benefits, including ensuring feature consistency between training and serving, simplifying feature reuse across models or teams, and enabling low-latency feature serving. These capabilities make it an essential component for building robust machine learning pipelines. However, it does not handle tasks like hyperparameter tuning or completely eliminate the need for data lineage tracking.
- A. Correct.
This is correct because the Databricks Feature Store ensures that the same feature definitions are used during both training and inference, maintaining consistency.
- B. Incorrect.
This is incorrect because the Databricks Feature Store does not handle hyperparameter tuning; that is the responsibility of separate tools or frameworks.
- C. Correct.
This is correct because the Feature Store allows features to be stored centrally, making them reusable across multiple models or teams.
- D. Incorrect.
This is incorrect because the Feature Store does not eliminate the need for tracking data lineage; rather, it facilitates lineage tracking by maintaining metadata about the features.
- E. Correct.
This is correct because the Feature Store provides low-latency APIs to serve features during model inference, ensuring real-time or near-real-time performance.