AI0-001 AI Concepts and Foundations Practice Question
An AI model achieves high accuracy on training data but performs poorly on new test data. The data scientist suspects the model has memorized noise. Which technique directly adds a penalty term to the loss function to address this?
⚠ Common exam trap
CompTIA often tests the distinction between regularization techniques that modify the loss function (L2) versus those that modify the network architecture or data (dropout, batch normalization, data augmentation), so candidates mistakenly choose dropout because it is a well-known regularization method, even though it does not add a penalty term to the loss function.
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 regularization
L2 regularization (also known as weight decay) directly adds a penalty term proportional to the squared magnitude of the model's weights to the loss function. This discourages the model from fitting the noise in the training data by keeping weights small, thereby reducing overfitting and improving generalization to new test data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch normalization
Why it's wrong here
Incorrect; batch normalization normalizes layers' inputs, not a loss penalty.
- ✗
Data augmentation
Why it's wrong here
Incorrect; data augmentation increases data variety but does not add a loss penalty.
- ✗
Dropout
Why it's wrong here
Incorrect; dropout reduces overfitting by randomly disabling neurons, not by adding a penalty to the loss.
- ✓
L2 regularization
Why this is correct
Correct; L2 adds a penalty term proportional to squared weights.
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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.