Azure Data Scientists Associate Practice Exam Prep & Study Guide

The function primarily responsible for handling batch requests in a scoring script is the one typically associated with executing the model on multiple records at once, which is the run() function. In the context of Azure Machine Learning, the run() method is specifically designed to accept input data and return predictions for each record in that dataset.

When dealing with batch scoring, this function processes the entire batch of input data, allowing the model to generate predictions efficiently without needing to individually call the model for each record. This enables scalable and faster inference by utilizing the capabilities of the Azure environment to handle larger datasets effectively.

The other functions listed may have relevant roles in the overall model operation, but they do not specifically focus on handling batch requests the way run() does. For instance, the init() function is typically used for model initialization and setting up resources, while process_batch() and score() are not standard functions recognized in the context of Azure’s typical batch scoring process, making them less relevant here.