hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A data scientist observes that a gradient…
A data scientist observes that a gradient boosting model's performance on the validation set is significantly worse than on the training set. Which adjustment is MOST likely to reduce this gap?
⚠ Common exam trap
AWS often tests the misconception that increasing model complexity (e.g., deeper trees or more features) always improves performance, when in fact regularization techniques like lowering the learning rate are the correct response to overfitting.
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
✓
Reduce the learning rate and increase the number of estimators
The gap between training and validation performance indicates overfitting. Reducing the learning rate and increasing the number of estimators is a standard regularization technique in gradient boosting: a lower learning rate shrinks the contribution of each tree, forcing the model to learn more slowly and generalize better, while more trees allow the model to still capture complex patterns without overfitting to noise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum depth of trees
Why it's wrong here
Increasing depth tends to increase overfitting, widening the gap.
- ✓
Reduce the learning rate and increase the number of estimators
Why this is correct
A lower learning rate makes the model more robust, and more estimators compensate, often reducing overfitting.
- ✗
Increase the number of features
Why it's wrong here
Adding more features can increase overfitting if they are noisy.
- ✗
Increase the subsample ratio to 1.0
Why it's wrong here
Using all data each iteration (subsample=1.0) can increase overfitting; stochastic gradient boosting uses lower subsample to reduce overfitting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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