AI0-001 AI Models and Data Engineering Practice Question
Which THREE practices are recommended for versioning machine learning models in a production environment?
⚠ Common exam trap
CompTIA often tests the misconception that Git is suitable for versioning all artifacts, including large binary model files, when in fact Git's architecture is optimized for text diffs and cannot efficiently manage model binaries in a production ML pipeline.
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
✓
Use a model registry like MLflow or DVC.
Option A is correct because a dedicated model registry such as MLflow or DVC is purpose-built for tracking model artifacts, versions, and lifecycle stages, which is the recommended practice for production ML versioning. Option B is correct because storing metadata like hyperparameters and the training data hash ties each model version to its exact training configuration and dataset, enabling reproducibility and auditability. Option C is correct because automating deployment based on version tags ensures that only approved, traceable model versions reach production and keeps deployment consistent with the registry. Option D is not recommended because Git is designed for source code and text, not large binary model files, which bloat repositories and lack proper artifact lineage. Option E is wrong because discarding older models destroys rollback capability, reproducibility, and compliance auditing, and storage savings do not justify that risk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a model registry like MLflow or DVC.
Why this is correct
A model registry such as MLflow or DVC provides centralised, immutable version tracking, linking each model artefact to its training data, parameters and metrics. This satisfies the production requirement for reproducible lineage and rollback, letting teams promote or revert specific model versions without ambiguity.
- ✓
Store model metadata such as hyperparameters and training data hash.
Why this is correct
Storing metadata including hyperparameters and training data hashes creates an auditable lineage record, letting teams reproduce any deployed model and trace which dataset produced it. This satisfies the production constraint of reproducibility and rollback, since the artefact alone cannot reveal how it was trained or which data version it consumed.
- ✓
Automate model deployment based on version tags.
Why this is correct
Automating deployment from version tags ties each released artefact to an immutable, traceable identifier, so the exact model revision serving production can be reproduced or rolled back. This satisfies the stem's versioning requirement by removing manual, error-prone promotion steps.
- ✗
Use Git to version model binaries.
Why it's wrong here
Git tracks text diffs, so large binary model artefacts bloat repositories and cannot be meaningfully merged or diffed; dedicated model registries or artefact stores handle binaries. It is tempting because Git is the standard for versioning training code, notebooks and configuration alongside models.
- ✗
Keep only the latest model to save storage.
Why it's wrong here
Discarding prior models removes the ability to roll back a regression, reproduce past predictions or audit which artefact served traffic. It is tempting because pruning genuinely reduces storage cost and registry clutter once a model is fully retired and no rollback is required.
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.