AI0-001 AI Concepts and Foundations Practice Question
A data scientist is training a neural network to classify images of animals. The training accuracy is 99%, but validation accuracy is only 65%. Which technique should the data scientist use to address this issue?
⚠ Common exam trap
CompTIA often tests the distinction between techniques that improve training speed (batch normalization, learning rate tuning) versus those that improve generalization (dropout, regularization), and the trap here is that candidates may confuse overfitting with underfitting or assume that more training always helps.
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 to the network
The high training accuracy (99%) and low validation accuracy (65%) indicate overfitting, where the model memorizes the training data but fails to generalize. Adding dropout layers randomly drops neurons during training, which forces the network to learn more robust features and reduces overfitting. This technique is specifically designed to improve generalization without requiring more data or altering the learning rate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply batch normalization
Why it's wrong here
Batch normalization stabilizes training but does not directly combat overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to further overfitting, not reduce it.
- ✓
Add dropout layers to the network
Why this is correct
Dropout randomly deactivates neurons, which reduces overfitting by making the model less sensitive to specific weights.
- ✗
Increase the learning rate
Why it's wrong here
A higher learning rate may cause the model to converge faster but does not address overfitting.
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