AI0-001 AI Infrastructure and Technologies Practice Question
A data engineering team needs to orchestrate a complex ML pipeline that involves data extraction, transformation, model training, and deployment. They require scheduling, monitoring, and retry logic. Which MLOps tool is BEST suited for this task?
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
✓
Apache Airflow
Apache Airflow is a workflow orchestration tool that supports complex DAGs, scheduling, monitoring, and retries, making it ideal for ML pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Weights & Biases
Why it's wrong here
W&B is for experiment tracking and model management, not pipeline orchestration.
- ✗
Kubeflow
Why it's wrong here
Kubeflow is tempting as it provides components for orchestrating ML pipelines. However, its primary purpose is to deploy and manage an entire ML stack *on Kubernetes*, offering tools for notebooks, model serving, and hyperparameter tuning within that specific container orchestration system. The scenario describes a need for a dedicated orchestrator with scheduling, monitoring, and retry logic, without specifying a Kubernetes-native environment or requiring a full ML platform. Therefore, it is not the best fit for a general-purpose pipeline orchestration requirement.
- ✗
MLflow
Why it's wrong here
MLflow focuses on experiment tracking and model registry, not pipeline orchestration.
- ✓
Apache Airflow
Why this is correct
Airflow is a mature, flexible orchestrator for scheduling and monitoring complex pipelines.
About these practice questions
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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.