nerdexam
Microsoft

DP-100 · Question #324

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 h

The correct answer is B. No. Creating a datastore does not satisfy the requirement to deploy a locally cloned MLflow model to a batch endpoint - the model must first be registered 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 datastore in the workspace. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(23 responses)
  • A
    17% (4)
  • B
    83% (19)

Why each option

Creating a datastore does not satisfy the requirement to deploy a locally cloned MLflow model to a batch endpoint - the model must first be registered in the workspace.

AYes

Creating a datastore provides a storage connection but does not register the MLflow model in the workspace, which is the actual prerequisite for referencing the model in a batch deployment configuration.

BNoCorrect

Deploying a local MLflow model to a batch endpoint requires registering the model in the Azure ML workspace as a model asset via ml_client.models.create_or_update so the batch deployment can reference it by name and version. A datastore only establishes a connection to external storage and does not register any model artifact, leaving the deployment prerequisite unmet.

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

#Azure ML deployment#MLflow model#Batch endpoint#Datastore

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