nerdexam
Microsoft

DP-100 · Question #323

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…

The correct answer is A. Yes. The solution DOES meet the goal. Registering the model in the workspace is the required prerequisite for deploying it to a batch endpoint. Azure Machine Learning batch endpoints can only deploy models that are registered in the workspace's model registry. Once the MLflow model…

Train and deploy models

Question

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: Register the model in the workspace. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(48 responses)
  • A
    92% (44)
  • B
    8% (4)

Explanation

The solution DOES meet the goal. Registering the model in the workspace is the required prerequisite for deploying it to a batch endpoint. Azure Machine Learning batch endpoints can only deploy models that are registered in the workspace's model registry. Once the MLflow model cloned from the local repository is registered, it becomes available as a deployable asset that can be referenced in a batch deployment configuration.

Topics

#Model Registration#MLflow Models#Batch Endpoints#Azure Machine Learning

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