AI0-001 Machine Learning and Deep Learning Practice Question
A data scientist is training a neural network to classify images of handwritten digits. The model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the scientist apply first to address this issue?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and underfitting, and the trap here is that candidates may confuse increasing epochs (option B) as a solution to low validation accuracy, when in fact it exacerbates overfitting in this scenario.
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
✓
Apply L2 regularization to the network weights
The model shows high training accuracy (99%) but lower validation accuracy (85%), which is a classic sign of overfitting. L2 regularization (option C) adds a penalty term to the loss function proportional to the squared magnitude of the weights, discouraging the network from learning overly complex patterns that do not generalize. This directly addresses overfitting without reducing the model's capacity too aggressively.
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 one or more hidden layers from the network
Why it's wrong here
Removing layers may cause underfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
Increasing epochs typically increases overfitting.
- ✓
Apply L2 regularization to the network weights
Why this is correct
L2 regularization penalizes large weights and reduces overfitting.
- ✗
Add more features to the input data
Why it's wrong here
Adding features is not a primary remedy for overfitting in image classification.
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Written by Johnson Ajibi, MSc IT Security
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