AI0-001 AI Implementation and Operations Practice Question
A data science team uses Git for version control of model code and DVC for data versioning. They want to implement a model registry to track trained models, their hyperparameters, and performance metrics. Which tool is specifically designed for this purpose and integrates with the existing workflow?
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
✓
MLflow Model Registry
MLflow Model Registry is specifically designed for managing model versions, tracking metadata, and integrating with Git and DVC. Apache Airflow is for workflow orchestration, not model registry. Kubernetes is for container orchestration. Docker is for containerization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apache Airflow
Why it's wrong here
Airflow is a workflow scheduler, not a model registry.
- ✗
Docker
Why it's wrong here
Docker creates containers, not a model registry.
- ✓
MLflow Model Registry
Why this is correct
MLflow provides a model registry that stores model versions and metadata.
- ✗
Kubernetes
Why it's wrong here
Kubernetes is for container orchestration, not model versioning.
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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.