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 named Workspace1. Workspace1 has a registered MLflow model named model1 with PyFunc flavor. You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2. You have the following code: You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully. Solution: Add the code_path parameter. Does the solution meet the goal?

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B. No The proposed solution does not satisfy the no-egress deployment requirement because deploying an MLflow model with PyFunc flavor may trigger external package downloads at runtime to resolve conda/pip dependencies, which fail when egress connectivity is disabled. To deploy successfully without egress, the model environment must be pre-built and all dependencies must be bundled into a custom Docker image or environment, not simply addressed by a ManagedOnlineDeployment parameter alone.

Answer

A. Yes Accepting the solution as sufficient ignores that MLflow PyFunc models with external dependencies will fail environment setup when outbound internet access is blocked on the managed endpoint.

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…

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