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.
Which TWO metrics are suitable for evaluating a regression model? (Select TWO.)
Accuracy
Why wrong: Accuracy is for classification problems.
Root Mean Squared Error (RMSE)
RMSE measures average prediction error in regression.
R-squared
R-squared indicates proportion of variance explained.
F1-score
Why wrong: F1-score is for classification.
Precision
Why wrong: Precision is for classification.
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)
Root Mean Squared Error (RMSE) is a standard metric for regression models because it measures the average magnitude of prediction errors in the same units as the target variable. It penalizes larger errors more heavily due to squaring, making it sensitive to outliers and useful for comparing 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.
Accuracy
Why it's wrong here
Accuracy is for classification problems.
Root Mean Squared Error (RMSE)
Why this is correct
RMSE measures average prediction error in regression.
Related concept
Read the scenario before looking for a memorised answer.
R-squared
Why this is correct
R-squared indicates proportion of variance explained.
Related concept
Read the scenario before looking for a memorised answer.
F1-score
Why it's wrong here
F1-score is for classification.
Precision
Why it's wrong here
Precision is for classification.
Common exam traps
Cisco often tests the distinction between classification and regression metrics, and the trap here is that candidates mistakenly apply classification metrics like Accuracy, F1-score, or Precision to regression problems because they are familiar with them from other contexts.
Detailed technical explanation
RMSE is calculated as the square root of the average of squared differences between predicted and actual values, which amplifies the impact of large errors. R-squared (coefficient of determination) represents the proportion of variance in the target variable explained by the model, ranging from 0 to 1 (or negative for poor fits). In practice, RMSE is scale-dependent, so it is best used to compare models on the same dataset, while R-squared provides a relative measure of fit that can be compared across different datasets.
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) — Root Mean Squared Error (RMSE) is a standard metric for regression models because it measures the average magnitude of prediction errors in the same units as the target variable. It penalizes larger errors more heavily due to squaring, making it sensitive to outliers and useful for comparing 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.
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Last reviewed: Jun 24, 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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