Designing and Implementing a Data Science Solution on Azure — Question 171

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have an Azure Machine Learning workspace that includes an AmlCompute cluster and a batch endpoint.

You clone a repository that contains an MLflow model to your local computer.

You need to ensure that you can deploy the model to the batch endpoint.

Solution: Create a datastore in the workspace.

Does the solution meet the goal?

Answer options

Correct answer: B

Explanation

Creating a datastore in the workspace does not directly facilitate the deployment of an MLflow model to a batch endpoint. The correct approach would typically involve registering the model and configuring the deployment settings rather than just creating a datastore.