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 company is deploying a machine learning model for fraud detection. The model outputs a probability score. The cost of false negatives is very high. Which TWO metrics should the company focus on optimizing?
Precision
Why wrong: Precision focuses on false positives, not false negatives.
False positive rate (FPR)
Why wrong: FPR is about false positives, not false negatives.
F1 score
F1 = harmonic mean of precision and recall; optimizing F1 also improves recall.
Area under the ROC curve (AUC-ROC)
Why wrong: AUC-ROC is a general measure, not specific to false negatives.
Recall
Recall = TP/(TP+FN); high recall means few false negatives.
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
Recall (true positive rate) measures ability to find positives; minimizing false negatives is optimizing recall. AUC-ROC summarizes overall performance but not specific to false negatives. Precision focuses on false positives. FPR is about false positives. F1 balances precision and recall, but recall directly addresses false negatives.
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.
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives.
False positive rate (FPR)
Why it's wrong here
FPR is about false positives, not false negatives.
F1 score
Why this is correct
F1 = harmonic mean of precision and recall; optimizing F1 also improves recall.
Related concept
OSPF neighbours must agree on key parameters.
Area under the ROC curve (AUC-ROC)
Why it's wrong here
AUC-ROC is a general measure, not specific to false negatives.
Recall
Why this is correct
Recall = TP/(TP+FN); high recall means few false negatives.
Related concept
OSPF neighbours must agree on key parameters.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 — Recall (true positive rate) measures ability to find positives; minimizing false negatives is optimizing recall. AUC-ROC summarizes overall performance but not specific to false negatives. Precision focuses on false positives. FPR is about false positives. F1 balances precision and recall, but recall directly addresses false negatives.
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
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