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Microsoft

DP-100 · Question #327

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 B. No. Creating a data asset does not enable deployment of a locally cloned MLflow model to a batch endpoint - the model itself must be registered as a model asset in the workspace.

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: Create a data asset in the workspace. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(25 responses)
  • A
    24% (6)
  • B
    76% (19)

Why each option

Creating a data asset does not enable deployment of a locally cloned MLflow model to a batch endpoint - the model itself must be registered as a model asset in the workspace.

AYes

A data asset registers a dataset reference for use as pipeline input or output, not a model artifact, and the batch endpoint deployment process requires a model asset registration - making this solution insufficient.

BNoCorrect

A batch endpoint deployment references a registered model asset, not a data asset. To make the local MLflow model deployable, it must be registered in the workspace using ml_client.models.create_or_update with the local path. A data asset represents input datasets, not a deployable model, so creating one does not fulfill the deployment prerequisite.

Concept tested: Deploying local MLflow model to batch endpoint

Source: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-deployments

Topics

#Model Deployment#Batch Endpoints#MLflow Models#Model Registration

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