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
Option B is correct because applying L2 regularization adds a penalty proportional to the squared magnitude of the model weights to the loss function, which constrains weight growth and directly reduces overfitting, helping the model generalize and maintain both precision and recall on unseen data. Option C is correct because dropout layers randomly deactivate a fraction of neurons during each training iteration, preventing the network from relying on specific co-adapted units and acting as an effective regularizer that lowers overfitting. Option E is correct because augmenting the 10,000-sample training set with synthetic examples increases data diversity and effective sample size, which is a standard technique to improve generalization and reduce overfitting when labeled data is limited. Option A is not appropriate because decreasing the learning rate only affects optimization step size and convergence stability, not the model's capacity to overfit, and can even slow convergence without regularizing. Option D is not appropriate because increasing the training batch size changes gradient estimation variance and training dynamics but does not by itself prevent overfitting; in fact, very large batches can sometimes harm 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.
- ✗
Decrease the learning rate
Why it's wrong here
Decreasing the learning rate slows weight updates but does nothing to constrain model capacity, so it cannot address overfitting on 10,000 labelled samples. It is tempting because learning-rate tuning genuinely stabilises training and helps convergence when loss oscillates or diverges — a legitimate fix for unstable optimisation, not for variance.
- ✓
Apply L2 regularization to the model weights
Why this is correct
L2 regularization adds a penalty proportional to squared weights to the loss, shrinking coefficients and limiting the model's ability to fit training noise. On 10,000 sentiment samples this constrains variance, directly satisfying the stated goal of minimising overfitting while preserving precision and recall.
- ✓
Use dropout layers in the neural network
Why this is correct
Dropout randomly deactivates units during each training step, preventing neurons from co-adapting to specific training examples. This stochastic regularisation reduces variance and overfitting on the 10,000-sample sentiment dataset, supporting the required high precision and recall on unseen data.
- ✗
Increase the training batch size
Why it's wrong here
Increasing batch size reduces gradient noise, which can sharpen convergence and worsen overfitting on 10,000 samples, working against the stated goal. It is tempting because larger batches speed up training on GPUs and stabilise loss curves, making it the right choice when throughput, not generalisation, is the constraint.
- ✓
Augment the training data with synthetic examples
Why this is correct
Augmenting with synthetic examples enlarges the effective training distribution, exposing the model to more varied phrasings than the original 10,000 samples contain. This reduces variance and overfitting, directly serving the requirement for high precision and recall on unseen sentiment data.
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Written by Johnson Ajibi, MSc IT Security
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