AI0-001 AI Implementation and Operations Practice Question
Exhibit
Refer to the exhibit. ``` Model: ResNet-50 Batch size: 32 Epochs: 10 Learning rate: 0.001 Optimizer: SGD Data: ImageNet subset Training accuracy: 0.99 Validation accuracy: 0.75 ```
A team trained a ResNet-50 model with the configuration shown. The high training accuracy and lower validation accuracy suggest overfitting. Which change to the training configuration is MOST likely to reduce overfitting?
⚠ Common exam trap
CompTIA often tests the misconception that increasing batch size or reducing epochs directly fixes overfitting, when in fact these changes can harm convergence or underfit, while regularization techniques like dropout are the correct solution.
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 dropout layers after convolutional layers.
Adding dropout layers after convolutional layers is a regularization technique that randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces overfitting. This directly addresses the symptom of high training accuracy with lower validation accuracy by preventing the model from relying too heavily on specific neurons.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce number of epochs to 5.
Why it's wrong here
Fewer epochs may underfit, but overfitting is already present.
- ✗
Increase batch size to 64.
Why it's wrong here
Larger batch size often leads to sharper minima, potentially more overfitting.
- ✗
Increase learning rate to 0.01.
Why it's wrong here
Higher learning rate may cause training to diverge.
- ✓
Add dropout layers after convolutional layers.
Why this is correct
Dropout randomly drops neurons, reducing co-adaptation.
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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.