Model Registry & Versioning — AI-300 Practice Question
A representative Microsoft Machine Learning Operations Engineer Associate (AI-300) exam question on Model Registry & Versioning. 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 B. Register all candidate models in the Azure ML model registry with distinct version tags, and store training parameters and metrics as model properties or tags in the registry.
Azure ML's model registry provides native version control, metadata tracking, and rollback capabilities essential for production lifecycle management. Registering models with distinct versions enables reproducibility and easy rollback. B directly supports model versioning and lineage tracking as documented in the model management concept guide. A lacks automated tracking and introduces manual error. D uses MLflow locally but bypasses Azure ML's registry versioning and governance. C treats models as code artifacts, which doesn't provide the specialized model metadata, metrics, and deployment integration that the ML registry offers.
The Question
You are preparing a machine learning model for production deployment in Azure Machine Learning. Your team has trained three candidate models using different algorithms and hyperparameters. You need to ensure that all models are tracked consistently, including their training parameters, metrics, and data lineage. Which approach best ensures reproducibility and enables rollback to a previous model version if the current production model underperforms?
Why B is correct
Azure ML's model registry provides native version control, metadata tracking, and rollback capabilities essential for production lifecycle management. Registering models with distinct versions enables reproducibility and easy rollback. B directly supports model versioning and lineage tracking as documented in the model management concept guide. A lacks automated tracking and introduces manual error. D uses MLflow locally but bypasses Azure ML's registry versioning and governance. C treats models as code artifacts, which doesn't provide the specialized model metadata, metrics, and deployment integration that the ML registry offers.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (B) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (B) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (B) is the only one that fully meets every constraint stated in the question.
Key idea: Model Registry & Versioning
Azure ML's model registry provides native version control, metadata tracking, and rollback capabilities essential for production lifecycle management. Registering models with distinct versions enables reproducibility and easy rollback. B directly supports model versioning and lineage tracking as documented in the model management concept guide. A lacks automated tracking and introduces manual error. D uses MLflow locally but bypasses Azure ML's registry versioning and governance. C treats models as code artifacts, which doesn't provide the specialized model metadata, metrics, and deployment integration that the ML registry offers. On the AI-300 exam, questions in the "Manage the machine learning model lifecycle and operations" 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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