Databricks Machine Learning Associate Question 451
Select 3You are building a recommendation system for an e-commerce platform using Databricks. The system needs to quickly retrieve the most recent user interaction data (e.g., items added to cart) during live user sessions, while also training machine learning models using aggregated historical data (e.g., user purchase trends). Which of the following statements accurately describe the roles of online and offline feature tables in this scenario?
- A
Online feature tables are optimized for low-latency, real-time lookups of feature data.
- B
Offline feature tables are primarily used for serving feature data during real-time predictions.
- C
Offline feature tables are designed to store historical feature data for batch processing and model training.
- D
Online feature tables are typically used to store pre-computed features for use in training machine learning models.
- E
Online feature tables play a critical role in supporting live inference during user interactions with the system.
Show answer and explanation
Correct answers: A, C, E
Explanation
Online feature tables are designed for serving real-time predictions with low latency, making them critical for scenarios involving live inference, like a recommendation system during user sessions. Offline feature tables, on the other hand, store historical feature data used for batch processing and model training. Understanding the distinction between the two is essential for designing scalable and efficient machine learning systems.
- A. Correct.
Correct: Online feature tables are specifically optimized for low-latency, real-time lookup scenarios, such as retrieving user interaction data during live sessions.
- B. Incorrect.
Incorrect: Offline feature tables are not used for real-time predictions but rather for storing historical data that is used in model training and batch processing.
- C. Correct.
Correct: Offline feature tables store large volumes of historical feature data, which are crucial for batch processing and training machine learning models.
- D. Incorrect.
Incorrect: Online feature tables are not typically used for storing pre-computed features for training but are used for serving real-time predictions.
- E. Correct.
Correct: Online feature tables are critical for serving feature data with low latency during live inference, such as during user interactions in a recommendation system.