AI0-001 AI Concepts and Foundations Practice Question
A company uses an AI model to screen job applications. The model is trained on historical hiring data that reflects past biases. After deployment, the model disproportionately rejects candidates from certain demographics. Which concept does this best illustrate?
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
✓
Algorithmic bias
Algorithmic bias refers to systematic and unfair discrimination in AI outputs due to biased training data or model design. Option A (overfitting) is about a model that performs well on training data but poorly on new data due to excessive complexity. Option B (model drift) is about performance degradation over time due to changes in data distribution. Option D (underfitting) is when a model is too simple to capture patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Overfitting
Why it's wrong here
Overfitting concerns variance to training samples, not systematic rejection of demographic groups; the model reproduces the bias encoded in its labels. It is tempting because overfitting harms generalisation, and would be correct if the model performed well on training data but poorly on new applicants generally.
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Model drift
Why it's wrong here
Model drift is performance decay as live data distributions shift over time, whereas this bias is present from deployment because the training labels encoded it. It is tempting because drift also degrades deployed models, and would be correct if rejection rates rose gradually as applicant patterns changed.
- ✓
Algorithmic bias
Why this is correct
Training on historical hiring data encodes past human decisions, so the model reproduces and amplifies those demographic disparities. This is algorithmic bias: systematic unfair outcomes arising from biased training data rather than explicit discriminatory rules.
- ✗
Underfitting
Why it's wrong here
Underfitting yields uniformly poor predictions across all groups, not disproportionate rejection of particular demographics. It is tempting because underfitting is a common training failure, and would be correct if the model had failed to capture the underlying patterns in the hiring data at all.
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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.