AI0-001 AI Concepts and Techniques Practice Question
A data scientist is using a linear regression model to predict house prices and observes that the model performs well on training data but poorly on test data. Which regularisation technique is MOST appropriate to reduce overfitting?
⚠ Common exam trap
CompTIA often tests the distinction between L1 and L2 regularisation by presenting a scenario where feature selection is not needed, and candidates mistakenly choose Lasso because they confuse 'reducing coefficients' with 'eliminating coefficients'.
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
✓
L2 regularisation (Ridge)
L2 regularisation (Ridge) adds a penalty term equal to the sum of the squared coefficients to the loss function, which shrinks coefficient magnitudes without forcing them to zero. This reduces variance and overfitting by making the model less sensitive to individual features, which is ideal when the model performs well on training data but poorly on test data due to high variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
L1 regularisation (Lasso)
Why it's wrong here
L1 can zero out coefficients, which may be too aggressive if all features are relevant; L2 is generally safer for reducing variance without losing features.
- ✗
Dropout
Why it's wrong here
Dropout is used in neural networks, not linear regression.
- ✓
L2 regularisation (Ridge)
Why this is correct
Ridge adds squared magnitude penalty, shrinking coefficients smoothly, which helps generalise.
- ✗
Data augmentation
Why it's wrong here
Data augmentation increases training data, but the question asks for a regularisation technique.
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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.