AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is 'model registry' in Azure Machine Learning?
⚠ Common exam trap
Watch out — candidates often confuse the model registry with a marketplace or compliance tool, but the exam specifically tests the registry's role as a versioned repository for managing model artifacts and their lineage.
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 centralised versioned store for tracking and managing trained models and their lineage
The model registry in Azure Machine Learning is a centralized, versioned store that tracks trained models along with their metadata, lineage, and lifecycle. It enables data scientists to register, version, and manage models, ensuring reproducibility and governance across the ML lifecycle.
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 public marketplace where organisations can buy pre-trained models from third parties
Why it's wrong here
A model marketplace such as Azure ML Catalog offers pre-built foundation models from vendors, whereas the model registry is an internal, controlled repository. It does not support purchasing or third-party transactions; it stores artifacts that your own training jobs produced, together with versioning and lineage. This option confuses a commercial ecosystem with a version-control system for your team's models.
- ✓
A centralised versioned store for tracking and managing trained models and their lineage
Why this is correct
The model registry is a central, versioned service within a machine learning workspace that tracks every registered model artifact, its metadata, and its lineage (training data, code, hyperparameters). It allows data scientists to compare versions, roll back to an earlier candidate, and promote models to production with auditable stage transitions. This is precisely the centralised, versioned store described in the question.
- ✗
A database of domain-specific vocabularies used for NLP model training
Why it's wrong here
A database of domain-specific vocabularies — e.g., medical or legal term lists — is a natural-language processing resource used for tokenization, named-entity recognition, or feature engineering. The model registry instead governs trained model objects, incrementing versions and capturing training metrics, dependencies, and deployment history. It does not store vocabulary terms or lexical knowledge bases.
- ✗
A compliance register documenting AI models used by an organisation for audit purposes
Why it's wrong here
A compliance register is a non-technical governance record that auditors use to document which models exist and who approved their use. The model registry is a technical MLOps component that supports model versioning, lineage, and deployment; it can provide evidence for audits, but it is not primarily a compliance document. This option treats a governance artifact as the core system itself.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.