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AI-900 Practice Question: Describe fundamental principles of machine learning on Azure

What is the Azure Machine Learning model registry?

⚠ Common exam trap

It's easy for candidates to confuse the model registry with model monitoring or deployment features, but the registry is purely a versioning and management store, not a runtime monitoring or purchasing system.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

A centralized repository for versioning, tracking, and managing trained ML models

The Azure Machine Learning model registry is a centralized repository within Azure Machine Learning that enables versioning, tracking, and management of trained machine learning models. It allows data scientists and MLOps engineers to register models with metadata, tags, and descriptions, and to manage multiple versions of the same model, facilitating reproducibility, collaboration, and deployment lifecycle management.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • A marketplace for purchasing pre-built AI models

    Why it's wrong here

    A marketplace for purchasing pre-built AI models is a commercial catalog such as Azure AI Catalog or partner solutions, where organizations discover third-party, fine-tuned, or industry-specific models. The model registry is an internal, private repository designed for an organization's own trained models, complete with version control and deployment metadata. It does not handle purchasing, licensing, or external model discovery.

  • A centralized repository for versioning, tracking, and managing trained ML models

    Why this is correct

    An Azure ML model registry is a centralized repository that stores trained models with immutable version numbers, full lineage metadata (training dataset, code, hyperparameters, metrics), and lifecycle stages. This enables reproducible experiments, controlled promotion from development to production, and governance through audit trails. The registry is essential to MLOps because it reconciles artifact management with deployment consistency, but it does not monitor live inference telemetry.

  • A compliance database for AI regulatory requirements

    Why it's wrong here

    A compliance database for AI regulatory requirements would manage legal obligations such as GDPR data subject rights, sector-specific rules, and audit evidence across an organization. In Azure, regulatory compliance is addressed by services like Microsoft Purview, Azure Policy, and Compliance Manager, not by the model registry. The model registry instead tracks technical model artifacts and version histories to support operational governance, not legal or regulatory conformity.

  • A system for monitoring models in production for data drift

    Why it's wrong here

    Monitoring models in production for data drift is performed by Azure ML model monitoring, which continuously analyzes inference data against training data distributions and triggers alerts on performance or drift metrics. The model registry is a storage and versioning system for model artifacts before and during deployment; it does not consume live telemetry or schedule retraining workflows. Confusing the two conflates artifact management with runtime observability.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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