Quick Answer
The answer is RMSE and MAPE, with RMSE being the primary choice when large errors are costly. RMSE squares each error before averaging, which disproportionately amplifies the impact of large deviations—exactly what you need when the business penalizes big mistakes, such as overpricing a house by hundreds of thousands of dollars. MAPE complements this by expressing errors as percentages, making it interpretable across different price scales, though it can be unstable when actual values are near zero. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how loss functions align with business objectives; a common trap is choosing MAE, which treats all errors equally and thus underestimates the cost of large errors. Remember: when large errors are costly, think “square the pain”—RMSE punishes outliers, while MAPE gives context. For a quick memory tip, recall that RMSE’s squared term acts like a magnifying glass on big mistakes, so if your business can’t afford a few huge misses, RMSE is your go-to metric.
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
Which TWO metrics are MOST appropriate for evaluating a regression model that predicts house prices, where the business is most sensitive to large errors?
Root Mean Squared Error (RMSE)
RMSE squares errors, so large errors are penalized heavily.
Mean Absolute Percentage Error (MAPE)
MAPE is scale-independent and penalizes large errors proportionally.
Accuracy
Why wrong: Accuracy is for classification.
Mean Absolute Error (MAE)
Why wrong: MAE does not penalize large errors more.
R-squared
Why wrong: R-squared measures variance explained, not error magnitude.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Root Mean Squared Error (RMSE)
RMSE is most appropriate because it squares the errors before averaging, which heavily penalizes large errors. Since the business is most sensitive to large errors in house price predictions, RMSE directly aligns with this requirement by amplifying the impact of outliers, making it a suitable metric for evaluating model performance in this context.
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.
Root Mean Squared Error (RMSE)
Why this is correct
RMSE squares errors, so large errors are penalized heavily.
Related concept
Read the scenario before looking for a memorised answer.
Mean Absolute Percentage Error (MAPE)
Why this is correct
MAPE is scale-independent and penalizes large errors proportionally.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is for classification.
Mean Absolute Error (MAE)
Why it's wrong here
MAE does not penalize large errors more.
R-squared
Why it's wrong here
R-squared measures variance explained, not error magnitude.
Common exam traps
The trap here is that candidates often choose MAE (Option D) because it is a common regression metric, but they fail to recognize that MAE does not penalize large errors more heavily, which is the key business requirement in this scenario.
Detailed technical explanation
RMSE is computed as the square root of the mean of squared differences between predicted and actual values, which gives higher weight to larger errors due to the squaring operation. In real-world house price prediction, a large error (e.g., predicting $500k instead of $1M) could lead to significant financial misallocation, making RMSE a critical metric for risk-sensitive applications. Note that RMSE is in the same units as the target variable (e.g., dollars), aiding interpretability, but it is sensitive to outliers, which is precisely the desired behavior here.
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: Root Mean Squared Error (RMSE) — RMSE is most appropriate because it squares the errors before averaging, which heavily penalizes large errors. Since the business is most sensitive to large errors in house price predictions, RMSE directly aligns with this requirement by amplifying the impact of outliers, making it a suitable metric for evaluating model performance in this context.
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 11, 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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