AI0-001 Machine Learning and Deep Learning Practice Question
A machine learning engineer is preparing to train a deep neural network for image classification. To avoid overfitting, which TWO techniques should the engineer apply? (Select TWO.)
⚠ Common exam trap
The CompTIA AI+ exam often tests the misconception that increasing model complexity (like adding layers) or reducing data helps with overfitting, when in reality these actions worsen it, while regularization and data augmentation are the correct countermeasures.
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
✓
Use dropout regularization.
Dropout regularization is a technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and reduces co-adaptation. This acts as a form of ensemble learning and significantly reduces overfitting by improving generalization.
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 dropout regularization.
Why this is correct
Dropout is a regularization technique that helps prevent overfitting by randomly dropping units.
- ✓
Use data augmentation.
Why this is correct
Data augmentation increases the diversity of training data, reducing overfitting.
- ✗
Increase the number of layers.
Why it's wrong here
Adding more layers increases model capacity, which can exacerbate overfitting.
- ✗
Remove all non-linear activation functions.
Why it's wrong here
Removing non-linearities makes the network effectively linear, limiting its ability to learn complex patterns and often causing underfitting.
- ✗
Reduce the training dataset size.
Why it's wrong here
Reducing data typically worsens overfitting as the model has less information to generalize from.
About these practice questions
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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.