AI0-001 AI Models and Data Engineering Practice Question
During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?
⚠ Common exam trap
CompTIA often tests the distinction between multicollinearity and overfitting, trapping candidates who confuse feature redundancy with model complexity.
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
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Multicollinearity
Creating a new feature as a linear combination of two existing features introduces perfect multicollinearity, where the new feature is an exact linear function of the original ones. This violates the assumption of no perfect multicollinearity in linear models, causing the design matrix to become singular and making coefficient estimates unstable or impossible to compute. Even in non-linear models, high multicollinearity can inflate variance and reduce interpretability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Multicollinearity
Why this is correct
Multicollinearity occurs when features are highly correlated, causing unstable estimates and inflated variances.
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Data leakage
Why it's wrong here
Data leakage involves using future information; feature correlation is internal.
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Overfitting
Why it's wrong here
Overfitting is capturing noise; multicollinearity increases variance but is distinct from overfitting.
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Underfitting
Why it's wrong here
Underfitting is due to insufficient model capacity, not feature correlation.
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Written by Johnson Ajibi, MSc IT Security
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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.