Quick Answer
The answer is to prune the decision tree. A deep tree that scores 100% on training data but fails on the test set is a classic case of overfitting, where the model has memorized noise and specific patterns rather than learning the underlying signal. Decision tree pruning directly addresses this by cutting away branches that offer little predictive power, reducing complexity and forcing the model to generalize better to unseen data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of bias-variance tradeoff and regularization in tree-based models; a common trap is choosing to increase tree depth or add more features, which would worsen overfitting. Remember the memory tip: "If your tree is too deep, it's time to prune—don't let it memorize, let it learn."
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is using a decision tree algorithm for a classification task. The tree is very deep and achieves 100% accuracy on the training set but performs poorly on the test set. Which technique should the data scientist use to improve generalization?
Add more features to the dataset.
Why wrong: More features can lead to overfitting if not regularized.
Reduce the number of training samples.
Why wrong: Reducing data typically worsens performance and increases overfitting risk.
Prune the decision tree.
Pruning reduces tree complexity and improves generalization.
Increase the maximum depth of the tree.
Why wrong: Increasing depth increases overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Prune the decision tree.
A deep decision tree that achieves 100% training accuracy but poor test accuracy is overfitting the training data. Pruning the tree removes branches that have little statistical power, reducing complexity and improving generalization to unseen data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Add more features to the dataset.
Why it's wrong here
More features can lead to overfitting if not regularized.
Reduce the number of training samples.
Why it's wrong here
Reducing data typically worsens performance and increases overfitting risk.
Prune the decision tree.
Why this is correct
Pruning reduces tree complexity and improves generalization.
Related concept
Read the scenario before looking for a memorised answer.
Increase the maximum depth of the tree.
Why it's wrong here
Increasing depth increases overfitting.
Common exam traps
The trap here is that candidates may confuse overfitting with underfitting and choose to increase model complexity (Option D) or add features (Option A), when the correct remedy for overfitting is to reduce complexity through pruning.
Detailed technical explanation
Decision tree pruning can be performed using cost-complexity pruning (also known as weakest-link pruning), where a complexity parameter (alpha) penalizes tree size. The algorithm iteratively collapses internal nodes that contribute the least to overall accuracy, selecting the subtree that minimizes the sum of misclassification cost plus alpha times the number of leaf nodes. In practice, cross-validation is used to choose the optimal alpha value that balances bias and variance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Prune the decision tree. — A deep decision tree that achieves 100% training accuracy but poor test accuracy is overfitting the training data. Pruning the tree removes branches that have little statistical power, reducing complexity and improving generalization to unseen data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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