AIF-C01 Fundamentals of AI and ML Practice Question
Which TWO techniques are commonly used to prevent overfitting in machine learning models? (Select TWO.)
⚠ Common exam trap
AWS often tests the misconception that adding more data or features always helps model performance, when in fact irrelevant features or reducing training data can worsen overfitting, and candidates may incorrectly associate 'more complexity' with better generalization.
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
✓
Use cross-validation
Cross-validation helps prevent overfitting by partitioning the training data into multiple folds, training the model on different subsets, and validating on held-out portions. This provides a more robust estimate of model performance on unseen data and reduces the risk of memorizing noise in a single train-test split.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more irrelevant features
Why it's wrong here
Irrelevant features introduce noise and can lead to overfitting.
- ✓
Use cross-validation
Why this is correct
Cross-validation helps assess model generalization and can indicate overfitting.
- ✗
Increase model complexity
Why it's wrong here
Increasing model complexity, such as adding more layers or parameters, directly increases variance and exacerbates overfitting by allowing the model to memorise training noise rather than generalising patterns. This option is tempting because, in scenarios where the model is underfitting (high bias), raising complexity can improve performance on training data, but it fails here because the stem specifically asks for techniques that *prevent* overfitting, not those that cause it.
- ✗
Reduce the amount of training data
Why it's wrong here
Less training data makes overfitting more likely.
- ✓
Use regularization
Why this is correct
Regularization adds a penalty to the loss function to limit model complexity.
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