AI0-001 AI Models and Data Engineering Practice Question
A team is training a deep neural network on a large image dataset. They observe that the training loss decreases smoothly but validation loss oscillates. Which regularization technique should be applied?
⚠ Common exam trap
Test-takers frequently confuse batch normalization as a regularization technique because it can reduce overfitting slightly due to its noise injection, but it is primarily for training stability, not a dedicated regularizer like dropout.
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
✓
Dropout
Dropout is the correct regularization technique because it randomly drops neurons during training, which prevents co-adaptation of features and reduces overfitting. This addresses the validation loss oscillation (a sign of overfitting) while allowing the training loss to decrease smoothly, as dropout only applies during training and not during validation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data augmentation
Why it's wrong here
Data augmentation increases the effective training set size, which helps generalization but is not a regularization technique applied within the network.
- ✗
L1 regularization
Why it's wrong here
L1 regularization encourages sparsity but is less effective than dropout for deep networks.
- ✓
Dropout
Why this is correct
Dropout reduces overfitting by randomly dropping units during training, forcing the network to learn robust features.
- ✗
Batch normalization
Why it's wrong here
Batch normalization mainly stabilizes training and allows higher learning rates, but it is not primarily for 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.