AI0-001 AI Models and Data Engineering Practice Question
A data scientist is building a regression model to predict house prices. The dataset contains features such as square footage, number of bedrooms, and year built. Initial model performance is poor, and the scientist suspects that feature engineering could help. Which approach is most likely to improve model accuracy?
⚠ Common exam trap
CompTIA often tests the misconception that adding more features always causes overfitting, when in fact carefully engineered interaction terms can reduce bias without excessive variance if regularized properly.
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
✓
Create interaction terms such as bedrooms times square footage
Creating interaction terms like bedrooms × square footage captures non-linear relationships and synergies between features that a linear model alone cannot represent. In real estate, the effect of square footage on price often depends on the number of bedrooms (e.g., a large house with few bedrooms may be less valuable), so interaction terms allow the model to learn these conditional patterns, directly improving predictive accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use only linear features because polynomial terms overfit
Why it's wrong here
Polynomial features can capture non-linearity, but the question asks for interaction terms; overfitting can be controlled.
- ✗
Remove all features except square footage to reduce noise
Why it's wrong here
Removing features reduces information and typically worsens performance.
- ✓
Create interaction terms such as bedrooms times square footage
Why this is correct
Interaction terms capture combined effects of features, often improving regression models.
- ✗
Add random noise to the target variable to increase variance
Why it's wrong here
Adding noise to the target degrades the signal and reduces accuracy.
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Written by Johnson Ajibi, MSc IT Security
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