Quick Answer
The correct answer is R-squared and Root Mean Squared Error (RMSE). These two evaluation metrics for regression models are appropriate because RMSE quantifies the average magnitude of prediction errors in the same units as the target variable, while R-squared measures the proportion of variance in the dependent variable that is explained by the independent variables. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this distinction tests your ability to differentiate regression metrics from classification metrics—a common trap is confusing RMSE with F1 or AUC, which are reserved for classification tasks. For example, F1 score balances precision and recall, and AUC evaluates the area under the ROC curve, both irrelevant for continuous outputs. A practical memory tip: think of regression as predicting numbers, so you need metrics that measure error size (RMSE) and fit quality (R-squared); classification predicts categories, so you need metrics like precision and recall.
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 regression model. Which TWO metrics are appropriate for evaluating regression performance?
Root Mean Squared Error (RMSE)
RMSE measures average prediction error.
F1 score
Why wrong: F1 score is for classification.
Area Under the ROC Curve (AUC)
Why wrong: AUC is for classification.
R-squared
R-squared measures proportion of variance explained.
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)
Correct options: C (Root Mean Squared Error) and D (R-squared). RMSE measures average error magnitude, R-squared measures variance explained. Option A (F1 score) is for classification. Option B (Precision) is for classification. Option E (AUC) is for classification.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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 measures average prediction error.
Related concept
OSPF neighbours must agree on key parameters.
F1 score
Why it's wrong here
F1 score is for classification.
Area Under the ROC Curve (AUC)
Why it's wrong here
AUC is for classification.
R-squared
Why this is correct
R-squared measures proportion of variance explained.
Related concept
OSPF neighbours must agree on key parameters.
Precision
Why it's wrong here
Precision is for classification.
Common exam traps
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
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FAQ
Modeling — This question tests Modeling — OSPF neighbours must agree on key parameters..
The correct answer is: Root Mean Squared Error (RMSE) — Correct options: C (Root Mean Squared Error) and D (R-squared). RMSE measures average error magnitude, R-squared measures variance explained. Option A (F1 score) is for classification. Option B (Precision) is for classification. Option E (AUC) is for classification.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
OSPF neighbours must agree on key parameters.
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Last reviewed: Jun 20, 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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