The answer is the F2 score (beta=2), which prioritizes recall over precision. This is correct because when false negatives are ten times costlier than false positives, the evaluation metric must emphasize minimizing missed failures, and the F-beta formula with beta=2 weights recall four times higher than precision, directly aligning with the asymmetric cost structure. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of cost-sensitive evaluation metrics for imbalanced data, where the common trap is choosing F1 (beta=1) or AUC-ROC, which ignore cost ratios. A reliable memory tip: remember that beta > 1 means you care more about catching failures (recall), so for a costlier false negative, "beta bigger, recall bigger."
AI0-001 AI Concepts and Foundations Practice Question
This AI0-001 practice question tests your understanding of ai concepts and foundations. 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.
An AI team is deploying a predictive maintenance model for industrial equipment. The model predicts failure within a 30-day window. The cost of a false positive is 10% of the cost of a false negative. Which evaluation metric should the team prioritize?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F2 score (beta=2) to prioritize recall over precision.
Given asymmetric costs, F-beta with beta>1 weights recall (false negatives) higher. Precision and recall individually ignore the cost trade-off. AUC-ROC summarizes performance but does not incorporate costs; F1 gives equal weight, which is not suitable when false negatives are costlier.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
F2 score (beta=2) to prioritize recall over precision.
Why this is correct
F2 score puts more weight on recall, aligning with the higher cost of false negatives.
Area under the ROC curve (AUC-ROC) to measure overall discrimination.
Why it's wrong here
AUC-ROC does not directly reflect the cost-sensitive decision threshold.
✗
F1 score to balance precision and recall equally.
Why it's wrong here
F1 gives equal weight, but false negatives are costlier than false positives.
✗
Precision to minimize false positives.
Why it's wrong here
False positives are less costly than false negatives, so recall is more important.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
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
How to think about this question
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.
KKey Concepts to Remember
OSPF neighbours must agree on key parameters.
Router ID selection can affect neighbour relationships and LSDB output.
OSPF cost influences the preferred path.
A route can appear in OSPF information but not become the installed route.
TExam Day Tips
→Check area mismatch first when OSPF adjacency fails.
→Review passive interfaces when a network is advertised but no neighbour forms.
→Use show ip ospf neighbor and show ip route clues carefully.
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
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
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 AI0-001 OSPF questions on adjacency and route selection.
AI Concepts and Foundations — This question tests AI Concepts and Foundations — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: F2 score (beta=2) to prioritize recall over precision. — Given asymmetric costs, F-beta with beta>1 weights recall (false negatives) higher. Precision and recall individually ignore the cost trade-off. AUC-ROC summarizes performance but does not incorporate costs; F1 gives equal weight, which is not suitable when false negatives are costlier.
What should I do if I get this AI0-001 question wrong?
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 AI0-001 OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An AI system is deployed to detect fraudulent transactions. The system flags 5% of transactions as fraudulent, but the actual fraud rate is 0.1%. The business sees many false positives and wants to reduce them without significantly increasing false negatives. Which metric should be prioritized for optimization?
hard
A.Recall
✓ B.F1 score
C.Accuracy
D.Precision
Why B: The F1 score balances precision and recall, making it ideal when false positives are costly but false negatives must not increase significantly. Optimizing precision alone would reduce false positives but could increase false negatives, while recall alone would not address the false positive problem. The F1 score ensures both metrics are jointly optimized, aligning with the business requirement.
Variation 2. An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?
medium
A.The model has no impact on fraud detection
B.The model detects all fraudulent transactions
✓ C.Many fraudulent transactions go undetected
D.Many legitimate transactions are flagged as fraud
Why C: High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.
Last reviewed: Jun 23, 2026
Question Discussion
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