MLS-C01 Modeling Practice Question
A data scientist is evaluating a linear regression model. Which TWO metrics are appropriate for evaluating the model's performance?
⚠ Common exam trap
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.
Answer choices
Why each option matters
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.
Answer analysis
Option-by-option breakdown
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.
- ✓
Root Mean Squared Error (RMSE)
Why this is correct
RMSE measures the average error magnitude, suitable for regression.
- ✗
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.
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Same concept, more angles
1 more way this is tested on MLS-C01
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?
easy- A.The model has high bias
- ✓ B.The model generalizes well with no severe overfitting
- C.The model is underfitting because R² is too high
- D.The model is overfitting because RMSE is lower on training data
Why 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.