AI0-001 AI Concepts and Techniques Practice Question
A team is deploying a sentiment analysis model that must achieve high precision and high recall. They have a labeled dataset of 10,000 samples. They want to minimize overfitting. Which THREE actions are most appropriate? (Select THREE.)
⚠ Common exam trap
CompTIA often tests the misconception that decreasing the learning rate is a regularization technique, when in fact it only affects optimization speed and not model complexity or overfitting prevention.
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 model weights
L2 regularization (option B) penalizes large weights by adding a squared magnitude term to the loss function, which discourages the model from fitting noise in the training data. This directly reduces overfitting while maintaining high precision and recall by keeping the decision boundary smooth and generalizable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the learning rate
Why it's wrong here
Learning rate affects training speed, not directly overfitting.
- ✓
Apply L2 regularization to the model weights
Why this is correct
Penalizes large weights, reducing overfitting.
- ✓
Use dropout layers in the neural network
Why this is correct
Randomly drops neurons during training, acting as regularization.
- ✗
Increase the training batch size
Why it's wrong here
Larger batch sizes can lead to sharper minima and may not reduce overfitting.
- ✓
Augment the training data with synthetic examples
Why this is correct
Increases effective dataset size, reducing overfitting.
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