Quick Answer
The answer is to decrease the maximum depth of each tree. This technique directly addresses overfitting, which occurs when a model memorizes noise in the training data rather than learning generalizable patterns, as evidenced by high training accuracy but poor test performance. By limiting tree depth, you constrain the complexity of individual decision trees, preventing them from splitting too finely and capturing spurious correlations—a core regularization strategy for Random Forest models on SageMaker. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of bias-variance tradeoff and common hyperparameter tuning; a frequent trap is choosing to increase the number of trees, which actually stabilizes predictions without reducing overfitting. Remember the mnemonic “Deep trees dig into noise, shallow trees stay general” to recall that reducing depth is the go-to fix for overfitting in ensemble models.
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 Random Forest model on Amazon SageMaker. The model performs well on the training set but poorly on the test set. Which technique should the data scientist use to address this issue?
Increase the number of trees in the forest
Why wrong: More trees can lead to overfitting if not regularized.
Decrease the maximum depth of each tree
Shallow trees generalize better, reducing overfitting.
Increase the learning rate
Why wrong: Learning rate is not a hyperparameter for Random Forest.
Increase the maximum depth of each tree
Why wrong: Deeper trees are more prone to 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
Decrease the maximum depth of each tree
The model is overfitting, as indicated by high training performance and poor test performance. Decreasing the maximum depth of each tree limits the complexity of individual trees, reducing overfitting by preventing them from memorizing noise in the training data. This is a standard regularization technique for Random Forest models in Amazon SageMaker.
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.
Increase the number of trees in the forest
Why it's wrong here
More trees can lead to overfitting if not regularized.
Decrease the maximum depth of each tree
Why this is correct
Shallow trees generalize better, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Increase the learning rate
Why it's wrong here
Learning rate is not a hyperparameter for Random Forest.
Increase the maximum depth of each tree
Why it's wrong here
Deeper trees are more prone to overfitting.
Common exam traps
AWS often tests the misconception that increasing model complexity (e.g., more trees or deeper trees) always improves performance, when in fact overfitting requires reducing complexity or applying regularization.
Detailed technical explanation
In Random Forest, each decision tree is typically grown to full depth without pruning, which can lead to high variance. Limiting max depth acts as a pre-pruning mechanism, controlling the bias-variance tradeoff. In Amazon SageMaker's built-in Random Forest algorithm (based on XGBoost's random forest mode), the `max_depth` parameter directly controls tree complexity, and reducing it from a high value (e.g., 30) to a moderate value (e.g., 10) often improves generalization on unseen 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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Decrease the maximum depth of each tree — The model is overfitting, as indicated by high training performance and poor test performance. Decreasing the maximum depth of each tree limits the complexity of individual trees, reducing overfitting by preventing them from memorizing noise in the training data. This is a standard regularization technique for Random Forest models in Amazon SageMaker.
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 30, 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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