Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A hospital deploys a machine learning model to screen patients for a rare disease. Only 0.1% of patients actually have the disease. The model correctly identifies most positive cases but also flags many healthy patients as potentially having the disease. The hospital wants to minimize the number of healthy patients who are incorrectly told they might have the disease. Which metric should the model optimize?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Recall
Recall measures how many actual positive cases are captured. Optimizing recall alone would likely increase false positives, which is what the hospital wants to minimize.
B
Best answer
Precision
Precision measures the accuracy of positive predictions. Maximizing precision reduces false positives, directly addressing the goal of minimizing unnecessary anxiety for healthy patients.
C
Distractor review
F1 score
F1 score balances precision and recall but does not specifically prioritize minimizing false positives over false negatives.
D
Distractor review
Accuracy
Accuracy can be misleading in highly imbalanced datasets. A model that predicts all patients as healthy would achieve 99.9% accuracy but would fail to find any positive cases, and it does not directly minimize false positives.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Precision — Precision measures the proportion of positive identifications that were actually correct. High precision means fewer false positives (healthy patients incorrectly told they have the disease). Recall measures the proportion of actual positives that were correctly identified. F1 score is a harmonic mean of precision and recall. Accuracy is misleading when classes are imbalanced. Since the priority is to minimize false positives, precision is the most appropriate metric.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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