Databricks Machine Learning Associate Question 94: You are tasked with scoring a pre-trained machine learning model... | Databricks Machine Learning Associate Dumps

Databricks Machine Learning Associate practice question 94 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 94

Select 3

You are tasked with scoring a pre-trained machine learning model using features stored in a Databricks Feature Store table. Which of the following steps are required to ensure the scoring process is successful?

A
Use the Feature Store Python API to load the feature table and join it with the inference data.
B
Manually extract feature data from the feature store table using SQL and pass it to the model.
C
Use the Feature Store's score_batch method to perform batch scoring with the model and feature table.
D
Ensure the model is registered in the MLflow Model Registry before using it for scoring.
E
Deploy the feature store table as a REST endpoint for real-time scoring.

Show answer and explanation

Correct answers: A, C, D

Explanation

To score a model using a feature store table, you must load and join the feature table with inference data using the Feature Store API. The score_batch method enables seamless batch scoring, and registering the model in the MLflow Model Registry ensures it is properly tracked and managed. Manually handling features or deploying them as a REST endpoint is unnecessary for batch scoring.

A. Correct.
Correct. The Feature Store Python API simplifies accessing and joining feature tables with inference data, ensuring proper alignment of features for the model.
B. Incorrect.
Incorrect. While SQL can extract data, manually handling features increases the risk of misalignment or inconsistency, which the Feature Store is designed to avoid.
C. Correct.
Correct. The score_batch method is a key function in the Feature Store API for scoring models with batch data, ensuring smooth integration between features and the model.
D. Correct.
Correct. Registering the model in the MLflow Model Registry is a best practice that ensures proper management and compatibility for downstream tasks like scoring.
E. Incorrect.
Incorrect. While deploying a REST endpoint may be suitable for real-time scoring, it is not required for batch scoring using the Feature Store.

Databricks Machine Learning Associate practice question 94 of 656

Databricks Machine Learning Associate Question 94

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