The answer is increasing the minimum number of samples required to split an internal node and reducing the maximum depth of each tree. These two actions directly combat random forest overfitting reduction by limiting how complex each individual decision tree can become. When trees are allowed to grow too deep or split on very few samples, they memorize noise and idiosyncrasies in the training data rather than learning generalizable patterns. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of variance control in ensemble methods—a common trap is assuming that adding more trees always reduces overfitting, when in fact it is tree depth and split constraints that matter most. Remember the memory tip: “Deep trees cheat, shallow trees treat”—keeping trees shallow and requiring more samples per split forces the model to generalize, directly reducing overfitting.
MLS-C01 Modeling Practice Question
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 training a random forest model for a binary classification task. The dataset has 100,000 samples and 500 features. The model is overfitting. Which TWO actions are MOST likely to reduce overfitting?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the maximum depth of each tree
Reducing the maximum depth of each tree limits the complexity of individual trees, preventing them from memorizing noise and specific patterns in the training data. This is a standard regularization technique for random forests that directly combats overfitting by controlling the variance of the model.
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.
✗
Increase the number of trees in the forest
Why it's wrong here
More trees usually improve generalization but do not directly reduce overfitting; it can actually increase if trees are overfit.
✓
Reduce the maximum depth of each tree
Why this is correct
Shorter trees are simpler and less likely to overfit.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
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
This can increase tree diversity but may not reduce overfitting; often it helps generalization.
✗
Use all features for each tree
Why it's wrong here
Using all features reduces tree diversity and may increase overfitting.
✓
Increase the minimum number of samples required to split an internal node
Why this is correct
Higher min samples split prevents learning from noise, reducing overfitting.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that adding more trees always reduces overfitting, but the trap here is that without controlling tree complexity (depth or split criteria), more trees can still produce an overfit ensemble, especially when individual trees are allowed to grow unchecked.
Detailed technical explanation
How to think about this question
Random forests reduce overfitting through two key mechanisms: bootstrapping (sampling with replacement) and random feature selection at each split. Limiting tree depth (max_depth) or increasing the minimum samples for a split (min_samples_split) directly controls the tree's capacity, preventing it from growing to purity on noisy data. In practice, hyperparameter tuning often uses cross-validation to find the optimal depth, as overly shallow trees can underfit while overly deep trees overfit.
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: Reduce the maximum depth of each tree — Reducing the maximum depth of each tree limits the complexity of individual trees, preventing them from memorizing noise and specific patterns in the training data. This is a standard regularization technique for random forests that directly combats overfitting by controlling the variance of the model.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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