- A
Equalized odds
Equalized odds requires that both true positive rate and false positive rate are equal across groups, directly addressing the false positive disparity.
- B
Individual fairness
Why wrong: Individual fairness requires similar predictions for similar individuals, not a group-level metric.
- C
Predictive parity
Why wrong: Predictive parity checks whether positive predictive value is equal across groups, which is about precision, not false positive rate.
- D
Disparate impact (80% rule)
Disparate impact compares the selection ratio between groups; a high false positive rate for males may affect selection ratio.
- E
Demographic parity
Why wrong: Demographic parity checks whether selection rates are equal, but does not consider false positive rates.
Question 449 of 1,000
mediumMultiple SelectObjective-mapped
AIF-C01 Practice Question: An AI system is used to screen job applications
This AIF-C01 practice question tests your understanding of an ai system is used to screen job applications. 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 system is used to screen job applications. The team finds that the model has a higher false positive rate for male applicants than female applicants. Which fairness metrics should they compute to quantify this disparity? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Equalized odds
Equalized odds requires equal true positive and false positive rates across groups. Disparate impact compares selection rates. Demographic parity does not consider error rates; predictive parity compares positive predictive values.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Equalized odds
Why this is correct
Equalized odds requires that both true positive rate and false positive rate are equal across groups, directly addressing the false positive disparity.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Individual fairness
Why it's wrong here
Individual fairness requires similar predictions for similar individuals, not a group-level metric.
- ✗
Predictive parity
Why it's wrong here
Predictive parity checks whether positive predictive value is equal across groups, which is about precision, not false positive rate.
- ✓
Disparate impact (80% rule)
Why this is correct
Disparate impact compares the selection ratio between groups; a high false positive rate for males may affect selection ratio.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Demographic parity
Why it's wrong here
Demographic parity checks whether selection rates are equal, but does not consider false positive rates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Individual fairness requires similar predictions for similar individuals, not a group-level metric.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
What to study next
Got this wrong? Here's your next step.
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Equalized odds — Equalized odds requires equal true positive and false positive rates across groups. Disparate impact compares selection rates. Demographic parity does not consider error rates; predictive parity compares positive predictive values.
What should I do if I get this AIF-C01 question wrong?
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
This AIF-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 AIF-C01 exam.
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