Databricks Machine Learning Associate Question 73
Select 3A data science team is working on a machine learning pipeline to predict customer churn. They decide to use Databricks Feature Store for feature management. Which benefits does using a Feature Store provide in this scenario?
- A
Centralized storage of features for consistent reuse across multiple ML models.
- B
Real-time feature computation during model inference without pre-computed features.
- C
Versioning of features to ensure reproducibility of ML experiments.
- D
Elimination of the need for feature engineering in the ML workflow.
- E
Simplified feature sharing across teams and projects.
Show answer and explanation
Correct answers: A, C, E
Explanation
Databricks Feature Store offers centralized storage, versioning, and simplified sharing of features, which are crucial for scalable and reproducible machine learning pipelines. It ensures consistency and efficiency in feature management but does not replace the need for feature engineering or focus on real-time computation.
- A. Correct.
Feature Store provides centralized storage for features, making them reusable across different models and ensuring consistency in feature computation.
- B. Incorrect.
While Feature Store supports precomputing and storing features, it does not inherently focus on real-time computation during inference.
- C. Correct.
Feature Store allows for versioning of features, which is valuable for ensuring reproducibility in machine learning experiments.
- D. Incorrect.
Feature Store does not eliminate the need for feature engineering but simplifies the management and reuse of features post-engineering.
- E. Correct.
Feature Store facilitates feature sharing, enabling collaboration across teams and projects while reducing duplication of effort.