AI0-001 AI Concepts and Foundations Practice Question
A financial analyst is using a linear regression model to predict housing prices based on square footage. The model's predictions are consistently off by a large margin for both very small and very large houses, while performing well for average-sized houses. Which phenomenon is most likely occurring?
⚠ Common exam trap
The trap here is misdiagnosing systematic errors at the extremes as overfitting or underfitting, when the specific pattern points to a wrong functional form.
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
✓
Non-linearity in the relationship
The model's errors are systematic at the extremes of the predictor range, which is a hallmark of assuming a linear relationship when the true relationship is non-linear. A linear regression cannot bend to fit curved patterns, so it underfits the tails. Overfitting would show high variance, underfitting would show poor fit everywhere, and multicollinearity requires multiple correlated predictors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Underfitting
Why it's wrong here
Underfitting happens when a model is too simple to capture the underlying pattern, leading to poor performance on both training and test data across the board. Here, the model performs well for average-sized houses, so it has captured some relationship. The errors are specific to the extremes, which points to a mismatch in functional form rather than overall simplicity.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity occurs when independent variables are highly correlated, leading to unstable coefficient estimates. In this scenario, there is only one predictor (square footage), so multicollinearity cannot be the issue. The errors are patterned, not random, which further rules out multicollinearity as the cause.
- ✗
Overfitting
Why it's wrong here
Overfitting occurs when a model captures noise in the training data and performs poorly on new data, often showing high variance. In this scenario, the model performs well on average-sized houses, suggesting it has learned the general trend. The systematic errors at the extremes indicate a different issue, not overfitting, which would typically affect all predictions unpredictably.
- ✓
Non-linearity in the relationship
Why this is correct
The pattern of errors—good fit in the middle but poor at extremes—suggests the true relationship between square footage and price is non-linear. A linear model cannot capture curvature, so it systematically under- or over-predicts at the tails. This is a classic sign of model misspecification due to assuming linearity when a polynomial or other non-linear form is needed.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.