Quick Answer
The answer is the Area Under the ROC Curve (AUC-ROC) using a one-vs-rest approach. This metric is most appropriate because it evaluates the model’s ability to rank classes correctly by measuring how well it distinguishes each class from all others across all probability thresholds. For a multi-class problem with 10 classes, the one-vs-rest AUC computes a separate ROC curve for each class, then averages them to produce a single score that reflects ranking quality rather than calibration or threshold-based accuracy. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that AUC-ROC is the go-to metric for ranking performance in multi-class settings, while log loss assesses probability calibration and F1 or accuracy ignore ranking entirely. A common trap is confusing log loss with ranking—remember that AUC-ROC cares about order, not exact probabilities. Memory tip: “AUC for ranking, log loss for calibration.”
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 data scientist is working on a multi-class classification problem with 10 classes. The model outputs probabilities and the scientist wants to evaluate the model's ability to rank classes correctly. Which metric is most appropriate?
F1 score
Why wrong: F1 score is not a ranking metric.
Accuracy
Why wrong: Accuracy does not consider ranking.
Area Under the ROC Curve (AUC-ROC)
AUC-ROC measures ranking ability for multi-class via one-vs-rest.
Log loss
Why wrong: Log loss measures probability calibration, not ranking.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Area Under the ROC Curve (AUC-ROC)
The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) measures the model's ability to distinguish between classes. For multi-class, one-vs-rest AUC can be used. Log loss measures calibration, not ranking. F1 score is for binary classification or per-class. Accuracy does not consider ranking. Option A: AUC-ROC is correct. Option B: Log loss measures probability calibration. Option C: F1 score is not a ranking metric. Option D: Accuracy is not a ranking metric.
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.
F1 score
Why it's wrong here
F1 score is not a ranking metric.
Accuracy
Why it's wrong here
Accuracy does not consider ranking.
Area Under the ROC Curve (AUC-ROC)
Log loss
Why it's wrong here
Log loss measures probability calibration, not ranking.
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: Area Under the ROC Curve (AUC-ROC) — The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) measures the model's ability to distinguish between classes. For multi-class, one-vs-rest AUC can be used. Log loss measures calibration, not ranking. F1 score is for binary classification or per-class. Accuracy does not consider ranking. Option A: AUC-ROC is correct. Option B: Log loss measures probability calibration. Option C: F1 score is not a ranking metric. Option D: Accuracy is not a ranking metric.
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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