Quick Answer
The answer is to increase regularization, reduce model complexity, and collect more training data. These three actions directly combat overfitting by addressing the root causes: regularization penalizes large coefficients to prevent the model from fitting noise, reducing complexity limits the model’s capacity to memorize spurious patterns, and adding more data improves generalization by providing a richer representation of the underlying distribution. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept frequently appears in scenario-based questions where a model shows high variance—a classic trap is confusing actions that increase complexity, like raising tree depth or adding polynomial features, with those that reduce it. Remember the mnemonic “RCD” for Regularization, Complexity, Data: when your validation error climbs, think RCD to keep your model aligned.
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 team is building a regression model to predict house prices. They observe that the model performs well on training data but poorly on validation data. Which THREE actions can help reduce overfitting? (Choose THREE.)
Reduce model complexity by selecting fewer features
Simpler models generalize better.
Increase regularization strength (e.g., L1, L2)
Regularization reduces overfitting by penalizing large weights.
Collect more training data if possible
More data reduces overfitting.
Increase the maximum depth of decision trees
Why wrong: Deeper trees overfit more.
Add more interaction features
Why wrong: More features can increase 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
Reduce model complexity by selecting fewer features
Option A (increase regularization) penalizes large coefficients. Option C (reduce model complexity) like using fewer features or a simpler algorithm. Option D (add more training data) helps generalization. Option B (increase tree depth) increases overfitting. Option E (feature engineering) may not reduce overfitting directly.
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.
Reduce model complexity by selecting fewer features
Why this is correct
Simpler models generalize better.
Related concept
Read the scenario before looking for a memorised answer.
Increase regularization strength (e.g., L1, L2)
Why this is correct
Regularization reduces overfitting by penalizing large weights.
Related concept
Read the scenario before looking for a memorised answer.
Collect more training data if possible
Why this is correct
More data reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Increase the maximum depth of decision trees
Why it's wrong here
Deeper trees overfit more.
Add more interaction features
Why it's wrong here
More features can increase overfitting.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce model complexity by selecting fewer features — Option A (increase regularization) penalizes large coefficients. Option C (reduce model complexity) like using fewer features or a simpler algorithm. Option D (add more training data) helps generalization. Option B (increase tree depth) increases overfitting. Option E (feature engineering) may not reduce overfitting directly.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is most likely to reduce overfitting?
mediumWhy A: Reducing the maximum depth of each tree limits the complexity of individual decision trees, preventing them from memorizing noise and specific patterns in the training data. This directly addresses overfitting by enforcing simpler, more generalized splits, which improves performance on unseen test data.
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Last reviewed: Jun 20, 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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