AI0-001 AI Concepts and Foundations Practice Question
Which TWO of the following are key stages in the AI lifecycle?
⚠ Common exam trap
Watch out — candidates often confuse optional or downstream activities (like retraining or manual feature extraction) with the mandatory, sequential stages of the AI lifecycle, leading them to select B or E instead of the correct deployment and data collection stages.
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
✓
Model deployment
Model deployment (C) is a key stage in the AI lifecycle because it involves integrating the trained model into a production environment where it can make predictions on live data. This stage is critical for operationalizing AI and delivering business value, following the model training and validation phases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Human annotation of all data
Why it's wrong here
Many AI systems use unsupervised or semi-supervised learning; not all data requires human annotation.
- ✗
Model retraining
Why it's wrong here
Retraining is important but is part of the monitoring and maintenance phase, not a core stage in a traditional lifecycle overview.
- ✓
Model deployment
Why this is correct
Deploying the model into a production environment is a critical phase.
- ✓
Data collection
Why this is correct
Data collection is the first step; without data no AI model can be built.
- ✗
Manual feature extraction
Why it's wrong here
Feature extraction is often automated, especially in deep learning; it is not a universal mandatory stage.
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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.