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.
Which TWO actions can help reduce overfitting in a decision tree model? (Choose 2.)
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 after training
Pruning the tree after training removes branches that have little predictive power, reducing overfitting by simplifying the model. This technique directly addresses the variance component of the bias-variance tradeoff, making the model generalize better to unseen data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Prune the tree after training
Why this is correct
Pruning removes branches that have little predictive power, reducing overfitting.
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 leads to more overfitting.
✓
Set a minimum number of samples per leaf
Why this is correct
Requiring more samples per leaf prevents the tree from learning noise.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of features considered at each split
Why it's wrong here
More features can increase overfitting.
✗
Use all training data without validation
Why it's wrong here
This does not address overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing model complexity (e.g., deeper trees or more features) always improves accuracy, when in fact it increases overfitting; candidates may incorrectly select options that add complexity instead of regularization.
Detailed technical explanation
How to think about this question
Decision tree pruning can be performed using cost-complexity pruning (also known as weakest-link pruning), where a complexity parameter (alpha) penalizes the number of leaf nodes. The optimal subtree is selected via cross-validation to minimize the misclassification rate plus alpha times the number of leaves. In scikit-learn, this is implemented via `ccp_alpha` in `DecisionTreeClassifier`. Setting a minimum number of samples per leaf acts as a pre-pruning constraint, forcing splits to have sufficient support, which reduces variance by preventing the tree from fitting noise in small partitions.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prune the tree after training — Pruning the tree after training removes branches that have little predictive power, reducing overfitting by simplifying the model. This technique directly addresses the variance component of the bias-variance tradeoff, making the model generalize better to unseen data.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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