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 orchestrates scheduled workflows and task dependencies; it stores no model artefacts, hyperparameters or metrics, so it cannot serve as the registry. It is tempting because it already sits in many ML pipelines, and would be the answer if the team needed to schedule and monitor training runs.
- ✗
Docker
Why it's wrong here
Docker packages code and dependencies into container images; it records no model versions, hyperparameters or performance metrics, so it cannot act as a registry. It is tempting because containers standardise training environments, which would be the answer if the team needed reproducible runtime packaging.
- ✓
MLflow Model Registry
Why this is correct
MLflow Model Registry is purpose-built to track trained models, their hyperparameters and performance metrics, and it integrates directly with Git and DVC workflows. It satisfies the stem's requirement for a dedicated registry rather than a general-purpose artefact store.
- ✗
Kubernetes
Why it's wrong here
Kubernetes schedules and scales containers across a cluster; it holds no model lineage, hyperparameters or metrics, so it cannot function as a registry. It is tempting because it commonly hosts model serving, and would be correct if the requirement were deploying and scaling inference workloads.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.