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.
A company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). Which metric is most appropriate for evaluating the model?
Accuracy
Why wrong: Accuracy can be misleading due to class imbalance.
Mean Squared Error
Why wrong: MSE is for regression, not classification.
F1-score
F1-score considers both precision and recall, suitable for imbalanced data.
Area Under the ROC Curve (AUC-ROC)
Why wrong: AUC-ROC is useful but can be optimistic with severe imbalance; precision-recall curve is often better.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
F1-score
Precision and recall (or F1-score) are more informative for imbalanced datasets than accuracy, because a model predicting all legitimate would achieve 99% accuracy but be useless. F1-score balances precision and recall.
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.
Accuracy
Why it's wrong here
Accuracy can be misleading due to class imbalance.
Mean Squared Error
Why it's wrong here
MSE is for regression, not classification.
F1-score
Why this is correct
F1-score considers both precision and recall, suitable for imbalanced data.
Related concept
OSPF neighbours must agree on key parameters.
Area Under the ROC Curve (AUC-ROC)
Why it's wrong here
AUC-ROC is useful but can be optimistic with severe imbalance; precision-recall curve is often better.
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: F1-score — Precision and recall (or F1-score) are more informative for imbalanced datasets than accuracy, because a model predicting all legitimate would achieve 99% accuracy but be useless. F1-score balances precision and recall.
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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