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.
A model registry like MLflow or DVC provides a centralized repository for tracking model versions, metadata, and lineage. This enables reproducibility, rollback, and auditability in production, which is essential for managing the lifecycle of machine learning models.
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
Model registries provide centralized versioning and lifecycle management.
- ✓
Store model metadata such as hyperparameters and training data hash.
Why this is correct
Metadata ensures reproducibility and traceability.
- ✓
Automate model deployment based on version tags.
Why this is correct
Automation ensures consistent and auditable deployments.
- ✗
Use Git to version model binaries.
Why it's wrong here
Git is designed for code, not large binary files.
- ✗
Keep only the latest model to save storage.
Why it's wrong here
Versioning requires keeping historical models for comparison and rollback.
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.