AI0-001 AI Infrastructure and Technologies Practice Question
A financial services company needs to deploy an ML model for loan approval that must be explainable to regulators. The model is a gradient boosting ensemble. They need to track experiments, log model parameters, and serve the model with explanations. Which THREE tools from the MLOps ecosystem should they use?
⚠ Common exam trap
CompTIA AI+ often tests the distinction between general infrastructure tools (like Kafka or Docker Compose) and purpose-built MLOps tools (like W&B, Kubeflow, and MLflow) that directly address experiment tracking, model serving, and explainability.
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
✓
Weights & Biases
Weights & Biases (W&B) is correct because it provides experiment tracking, hyperparameter logging, and model versioning, which are essential for regulatory explainability and auditability. It integrates directly with gradient boosting frameworks like XGBoost and LightGBM to log parameters and metrics, enabling reproducible ML pipelines. MLflow is correct because it offers experiment tracking, parameter and metric logging, a model registry, and model serving, allowing the team to track experiments and deploy the model with versioned artifacts. Kubeflow is correct because it provides an end-to-end MLOps platform for building, training, and serving models on Kubernetes, including pipeline and model-serving components that support explainability tooling. Together, these three purpose-built MLOps tools cover experiment tracking, parameter logging, and model serving with explanations, whereas Apache Kafka (a streaming platform) and Docker Compose (a container orchestration tool for local development) do not address these MLOps requirements.
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 Kafka
Why it's wrong here
Kafka is a streaming platform, not an MLOps tool for tracking/model serving.
- ✗
Docker Compose
Why it's wrong here
Docker Compose is for container orchestration, not ML experiment tracking.
- ✓
Weights & Biases
Why this is correct
W&B provides experiment logging, hyperparameter tracking, and model visualization.
- ✓
Kubeflow
Why this is correct
Kubeflow orchestrates ML pipelines and can deploy models with custom serving containers.
- ✓
MLflow
Why this is correct
MLflow tracks experiments, parameters, and models; it also has a model registry.
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.