AI0-001 AI Concepts and Foundations Practice Question
Exhibit
Refer to the exhibit.
{
"model": {
"layers": [
{"type": "Dense", "units": 256, "activation": "relu"},
{"type": "Dropout", "rate": 0.5},
{"type": "Dense", "units": 128, "activation": "relu"},
{"type": "Dropout", "rate": 0.5},
{"type": "Dense", "units": 10, "activation": "softmax"}
],
"optimizer": {"type": "Adam", "learning_rate": 0.001},
"loss": "categorical_crossentropy"
}
}A data scientist notices the model overfits. Which change to the exhibit's configuration would most likely reduce overfitting?
⚠ Common exam trap
CompTIA often tests the misconception that increasing model capacity (more units or layers) or removing regularization always improves performance, when in fact these changes exacerbate overfitting; candidates must recognize that regularization techniques like L2 are specifically designed to penalize complexity and reduce overfitting.
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
✓
Add L2 regularization to dense layers
Adding L2 regularization to dense layers penalizes large weights by adding a squared magnitude term to the loss function, which forces the model to learn simpler patterns and reduces overfitting. This directly addresses the core issue of the model memorizing noise in the training 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.
- ✗
Remove dropout layers
Why it's wrong here
Removing dropout would remove a regularization technique and likely increase overfitting.
- ✗
Increase learning rate to 0.01
Why it's wrong here
Increasing the learning rate may cause the model to converge too quickly or oscillate, often worsening overfitting.
- ✓
Add L2 regularization to dense layers
Why this is correct
L2 regularization adds a penalty on large weights, discouraging complex models and reducing overfitting.
- ✗
Increase units in the first dense layer to 512
Why it's wrong here
Increasing units increases model capacity, which typically worsens overfitting.
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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.