- A
Use bootstrap sampling with replacement
Why wrong: Bootstrap is inherent to random forest and not a variance reduction technique.
- B
Decrease the maximum depth of trees
Shallow trees reduce overfitting, lowering variance.
- C
Increase the minimum samples per leaf
Larger leaf samples reduce model complexity, reducing variance.
- D
Increase the number of trees in the forest
Why wrong: More trees typically stabilize predictions, reducing variance.
- E
Increase the number of features considered at each split
Why wrong: More features can increase variance.
Question 623 of 1,755
Two Effective Actions to Reduce Variance in Random Forest
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 regression. The model shows high variance on the validation set. Which TWO actions are most likely to reduce variance? (Choose 2.)
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 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
✓
Decrease the maximum depth of trees
Both decreasing the maximum depth of trees (B) and increasing the minimum samples per leaf (C) reduce the complexity of individual trees. Decreasing max depth limits tree growth, preventing overfitting to noise. Increasing min samples per leaf forces leaves to contain more samples, smoothing predictions and reducing variance. Together, these regularization techniques directly combat high variance in random forest models.
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.
- ✗
Use bootstrap sampling with replacement
Why it's wrong here
Bootstrap is inherent to random forest and not a variance reduction technique.
- ✓
Decrease the maximum depth of trees
Why this is correct
Shallow trees reduce overfitting, lowering variance.
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 minimum samples per leaf
Why this is correct
Larger leaf samples reduce model complexity, reducing variance.
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 trees in the forest
Why it's wrong here
More trees typically stabilize predictions, reducing variance.
- ✗
Increase the number of features considered at each split
Why it's wrong here
More features can increase variance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the misconception that adding more trees always reduces variance, but the trap here is that while more trees reduce variance from averaging, they do not address the root cause of overfitting from overly complex individual trees.
Detailed technical explanation
How to think about this question
In random forests, variance is primarily controlled by limiting tree complexity (e.g., max_depth, min_samples_leaf) and by increasing tree diversity through feature subsampling. Decreasing max_depth or increasing min_samples_leaf forces trees to be simpler, reducing their ability to memorize noise. A real-world scenario: on a dataset with many irrelevant features, deep trees can split on noise, causing high variance; capping depth or requiring more samples per leaf forces splits to be based on more generalizable patterns.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Decrease the maximum depth of trees — Both decreasing the maximum depth of trees (B) and increasing the minimum samples per leaf (C) reduce the complexity of individual trees. Decreasing max depth limits tree growth, preventing overfitting to noise. Increasing min samples per leaf forces leaves to contain more samples, smoothing predictions and reducing variance. Together, these regularization techniques directly combat high variance in random forest models.
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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Last reviewed: Jul 4, 2026
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