Quick Answer
Pruning the tree is the correct technique to address overfitting in a decision tree classifier. When a model performs well on training data but poorly on test data, it has memorized noise and specific patterns rather than learning generalizable rules, and pruning directly counters this by removing branches that have little statistical significance or capture noise. This concept is central to the AWS Certified Machine Learning Specialty MLS-C01 exam, where you must distinguish between overfitting remedies like pruning, regularization, or reducing tree depth, versus ineffective approaches like adding more features or increasing tree complexity. A common trap is confusing pruning with pre-pruning (early stopping) or thinking that more data alone fixes overfitting—pruning specifically targets the tree’s structure after growth. Memory tip: “Prune the poor performers” to recall that you cut away weak branches that only fit training noise, not real patterns.
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 training a decision tree classifier and notices that the model performs well on training data but poorly on test data. Which technique should the data scientist use to address this issue?
Use a different split criterion
Why wrong: Changing criterion may not fix overfitting.
Prune the tree
Pruning reduces overfitting.
Apply L1 regularization
Why wrong: L1 is not typical for decision trees.
Increase tree depth
Why wrong: Increasing depth worsens 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 tree
Pruning the tree reduces overfitting by removing branches that have little statistical significance or that capture noise in the training data. This technique improves generalization to unseen test data, which directly addresses the symptom of high training accuracy and low test accuracy.
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.
Use a different split criterion
Why it's wrong here
Changing criterion may not fix overfitting.
Prune the tree
Why this is correct
Pruning reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Apply L1 regularization
Why it's wrong here
L1 is not typical for decision trees.
Increase tree depth
Why it's wrong here
Increasing depth worsens overfitting.
Common exam traps
Cisco often tests the misconception that regularization techniques like L1/L2 apply universally, when in fact they are specific to models with learnable weights (e.g., linear regression, neural networks) and not to tree-based models.
Detailed technical explanation
Pruning can be pre-pruning (early stopping, e.g., limiting max depth or min samples per leaf) or post-pruning (e.g., cost-complexity pruning using a complexity parameter alpha). In scikit-learn, the `ccp_alpha` parameter implements minimal cost-complexity pruning, which trades off tree size against training error to find a subtree that generalizes better. A real-world scenario is credit risk modeling, where an unpruned tree might memorize rare default patterns that do not hold in new data.
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 tree — Pruning the tree reduces overfitting by removing branches that have little statistical significance or that capture noise in the training data. This technique improves generalization to unseen test data, which directly addresses the symptom of high training accuracy and low test accuracy.
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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