Quick Answer
The answer is Root Mean Squared Error (RMSE) and R-squared. RMSE directly quantifies the average prediction error in the same units as the target variable, making it intuitive for assessing model accuracy, while R-squared measures the proportion of variance in the dependent variable explained by the independent variables, ranging from 0 to 1 with higher values indicating better fit. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish regression metrics from classification metrics like accuracy or F1-score—a common trap is selecting Mean Absolute Error (MAE) instead of RMSE, but RMSE is preferred here because it penalizes larger errors more heavily, aligning with linear regression’s squared loss optimization. For a memory tip, think “R-squared for fit, RMSE for error in units”—if you remember that R-squared explains variance and RMSE measures deviation, you’ll avoid the classification metric pitfall.
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 evaluating a linear regression model. Which TWO metrics are appropriate for evaluating the model's performance?
R-squared
R-squared measures the proportion of variance explained by the model.
Root Mean Squared Error (RMSE)
RMSE measures the average error magnitude, suitable for regression.
Precision
Why wrong: Precision is for classification.
Area Under the ROC Curve (AUC-ROC)
Why wrong: AUC-ROC is for binary classification.
F1 score
Why wrong: F1 score is for classification problems.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
R-squared
R-squared is a standard metric for linear regression that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, making it directly appropriate for evaluating regression model performance.
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.
R-squared
Why this is correct
R-squared measures the proportion of variance explained by the model.
Related concept
Read the scenario before looking for a memorised answer.
Root Mean Squared Error (RMSE)
Why this is correct
RMSE measures the average error magnitude, suitable for regression.
Related concept
Read the scenario before looking for a memorised answer.
Precision
Why it's wrong here
Precision is for classification.
Area Under the ROC Curve (AUC-ROC)
Why it's wrong here
AUC-ROC is for binary classification.
F1 score
Why it's wrong here
F1 score is for classification problems.
Common exam traps
AWS often tests the distinction between regression and classification metrics, and the trap here is that candidates mistakenly apply classification metrics like Precision, AUC-ROC, or F1 score to a regression problem, not recognizing they are fundamentally incompatible with continuous outputs.
Detailed technical explanation
R-squared is calculated as 1 - (SS_res / SS_tot), where SS_res is the sum of squared residuals and SS_tot is the total sum of squares. A subtle behavior is that adding more predictors always increases R-squared, even if they are irrelevant, which is why adjusted R-squared is often preferred for model selection. In real-world scenarios, such as predicting house prices, R-squared helps quantify how well features like square footage and location explain price variance.
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: R-squared — R-squared is a standard metric for linear regression that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, making it directly appropriate for evaluating regression model performance.
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.
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 is evaluating a regression model. The RMSE on the training set is 2.5, and on the test set is 2.7. The R² on the test set is 0.98. What does this indicate?
easyWhy B: The model has low error and high R² on both sets, indicating good generalization without significant overfitting. The small difference between training and test RMSE suggests no severe overfitting.
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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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