Answer-first summary for fast verification
Answer: No
The solution does not meet the goal because creating a datastore alone is insufficient for deploying an MLflow model to a batch endpoint. To deploy a model, it must first be registered in the Azure ML workspace. Since the model is currently only available locally (cloned repository), the necessary step is to register the model in the workspace, which can be done using the MLflow SDK (e.g., `mlflow.register_model`). A datastore is used for managing data storage connections (e.g., to Azure Blob Storage), but it does not facilitate model registration or deployment directly. The community discussion unanimously supports 'No' (100% for option B), confirming that the proposed solution is inadequate.
Author: LeetQuiz Editorial Team
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You have an Azure Machine Learning workspace containing an AmlCompute cluster and a batch endpoint. You clone a repository with an MLflow model to your local computer.
You need to deploy the model to the batch endpoint.
Proposed solution: Create a datastore in the workspace.
Does this solution achieve the goal?
A
Yes
B
No
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