Quick Answer
The answer is MAE, because it is more robust to outliers. This robustness stems from the fact that mean absolute error (MAE) calculates the absolute difference between predicted and actual values, treating all errors linearly and equally. In contrast, mean squared error (MSE) squares these differences, which disproportionately amplifies the influence of large errors—making the model highly sensitive to extreme data points. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of loss function behavior under data imperfections, often appearing in scenario-based questions about regression model selection. A common trap is assuming MSE is always better due to its differentiability, but for datasets with outliers, MAE’s linear penalty prevents skewed model updates. Memory tip: think “Absolute = All errors equal,” while “Squared = Squared sensitivity to extremes.”
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 working on a regression problem with a dataset that contains outliers. The data scientist is choosing between mean squared error (MSE) and mean absolute error (MAE) as the loss function. Which loss function is more robust to outliers?
Both are equally robust to outliers.
Why wrong: MSE is more sensitive to outliers than MAE.
MSE, because it penalizes large errors more heavily.
Why wrong: Penalizing large errors more makes MSE more sensitive to outliers.
MAE, because it treats all errors equally.
MAE is linear in errors, reducing the impact of outliers.
Neither is robust; use Huber loss instead.
Why wrong: While Huber loss is robust, MAE is still more robust than MSE.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
MAE, because it treats all errors equally.
MAE is more robust to outliers because it uses the absolute difference between predicted and actual values, which does not disproportionately penalize large errors. In contrast, MSE squares the errors, causing outliers to have a much larger influence on the loss and model updates. This makes MAE less sensitive to extreme values in regression tasks.
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.
Both are equally robust to outliers.
Why it's wrong here
MSE is more sensitive to outliers than MAE.
MSE, because it penalizes large errors more heavily.
Why it's wrong here
Penalizing large errors more makes MSE more sensitive to outliers.
MAE, because it treats all errors equally.
Why this is correct
MAE is linear in errors, reducing the impact of outliers.
Related concept
Read the scenario before looking for a memorised answer.
Neither is robust; use Huber loss instead.
Why it's wrong here
While Huber loss is robust, MAE is still more robust than MSE.
Common exam traps
AWS often tests the misconception that a loss function that penalizes errors more heavily is better for robustness, when in fact the opposite is true for outliers.
Detailed technical explanation
Under the hood, MSE's squared error term creates a quadratic penalty that grows rapidly with error magnitude, causing the gradient to be dominated by outliers during backpropagation. MAE's linear penalty ensures each error contributes proportionally, but it has a non-differentiable point at zero, which can be handled with subgradients. In real-world scenarios like predicting house prices with occasional data entry errors, MAE often yields a model that is more stable and less skewed by anomalous records.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: MAE, because it treats all errors equally. — MAE is more robust to outliers because it uses the absolute difference between predicted and actual values, which does not disproportionately penalize large errors. In contrast, MSE squares the errors, causing outliers to have a much larger influence on the loss and model updates. This makes MAE less sensitive to extreme values in regression tasks.
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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