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Mlflow & Git — AI-300 Practice Question

A representative Microsoft Machine Learning Operations Engineer Associate (AI-300) exam question on Mlflow & Git. Work through it below, then read why each option is right or wrong.

This question is part of the free Microsoft Machine Learning Operations Engineer Associate practice test.

Short answer

The correct answer is A. Use the Azure Machine Learning SDK to log the active Git commit hash as a run property during training, ensuring the source code is checked into a locked branch before execution..

A is correct. Azure Machine Learning integrates with Git to track the specific commit hash used for a run. By ensuring the code is checked into a locked branch (e.g., via CI/CD gates) and logging the `git_commit` property during the run, you guarantee immutability and traceability. D allows manual overwrites which breaks the audit trail of code immutability. C relies on manual documentation which is error-prone. B suggests MLflow can track code without Git, but while MLflow has some snapshot capabilities, the standard practice for enterprise governance with AML is leveraging the Git integration to ensure the exact source code state is recorded.

The Question

A team is using MLflow for experiment tracking within Azure Machine Learning. They need to ensure that every model registered in the Production environment has an associated Git commit hash and that the training code used for that model is immutable. How should the integration be configured?

AUse the Azure Machine Learning SDK to log the active Git commit hash as a run property during training, ensuring the source code is checked into a locked branch before execution.Correct
BDisable Git integration entirely and use MLflow's internal versioning system, which automatically tracks code snapshots without requiring external source control.
CStore the model artifacts in Azure Blob Storage with versioning enabled and rely on users to manually document which code version produced each blob.
DConfigure the AML pipeline to automatically tag the model with the current date and allow manual overwrites of the Git hash if the code changes slightly.

Why A is correct

A is correct. Azure Machine Learning integrates with Git to track the specific commit hash used for a run. By ensuring the code is checked into a locked branch (e.g., via CI/CD gates) and logging the `git_commit` property during the run, you guarantee immutability and traceability. D allows manual overwrites which breaks the audit trail of code immutability. C relies on manual documentation which is error-prone. B suggests MLflow can track code without Git, but while MLflow has some snapshot capabilities, the standard practice for enterprise governance with AML is leveraging the Git integration to ensure the exact source code state is recorded.

Why the other options are wrong

Option B: Disable Git integration entirely and use MLflow's internal versioning system, which automatically tracks code snapshots without requiring external source control.

Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.

Option C: Store the model artifacts in Azure Blob Storage with versioning enabled and rely on users to manually document which code version produced each blob.

Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.

Option D: Configure the AML pipeline to automatically tag the model with the current date and allow manual overwrites of the Git hash if the code changes slightly.

Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.

Key idea: Mlflow & Git

A is correct. Azure Machine Learning integrates with Git to track the specific commit hash used for a run. By ensuring the code is checked into a locked branch (e.g., via CI/CD gates) and logging the `git_commit` property during the run, you guarantee immutability and traceability. D allows manual overwrites which breaks the audit trail of code immutability. C relies on manual documentation which is error-prone. B suggests MLflow can track code without Git, but while MLflow has some snapshot capabilities, the standard practice for enterprise governance with AML is leveraging the Git integration to ensure the exact source code state is recorded. On the AI-300 exam, questions in the "Design and implement an MLOps infrastructure" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.

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