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 an MLflow model, the model must first be registered in the Azure ML workspace model registry. Since the model is currently only available locally after cloning the repository, it needs to be uploaded and registered before deployment. The community discussion shows 100% consensus that the answer is 'No' (B), with upvoted comments confirming that datastore creation doesn't address the core requirement of model registration for batch endpoint deployment.
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 meet the goal?
A
Yes
B
No
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