AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Exhibit
Error: Model ‘EinsteinOCR’ fails fairness check.
- FPR by race: Group A 0.05, Group B 0.20
- FNR by race: Group A 0.02, Group B 0.12
Recommendation: Apply reweighting or collect more data for Group B.
Refer to the exhibit. A developer receives this fairness check error. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Error: Model ‘EinsteinOCR’ fails fairness check.
- FPR by race: Group A 0.05, Group B 0.20
- FNR by race: Group A 0.02, Group B 0.12
Recommendation: Apply reweighting or collect more data for Group B.
A
The model has higher false positive and false negative rates for Group B.
The higher rates for Group B indicate bias.
B
The error is due to insufficient training data for Group A.
Why wrong: Group A has lower rates, so insufficient data is not the issue.
C
The model is overfitting.
Why wrong: Overfitting is not shown by fairness metrics.
D
The recommendation suggests reweighting, so the model is already fair.
Why wrong: The recommendation implies the model is currently unfair.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model has higher false positive and false negative rates for Group B.
Option B is correct because the exhibit clearly shows significantly higher false positive and false negative rates for Group B, indicating the model treats groups differently. Option A is wrong because overfitting is not indicated by these metrics. Option C is wrong because the recommendation is to fix the issue, not that the model is already fair. Option D is wrong because Group A has lower rates, suggesting Group B's data may be insufficient, but the cause is the disparity itself.
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.
✓
The model has higher false positive and false negative rates for Group B.
Why this is correct
The higher rates for Group B indicate bias.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The error is due to insufficient training data for Group A.
Why it's wrong here
Group A has lower rates, so insufficient data is not the issue.
✗
The model is overfitting.
Why it's wrong here
Overfitting is not shown by fairness metrics.
✗
The recommendation suggests reweighting, so the model is already fair.
Why it's wrong here
The recommendation implies the model is currently unfair.
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
Command / output trap
Overfitting is not shown by fairness metrics.
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
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which AI Associate 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.
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model has higher false positive and false negative rates for Group B. — Option B is correct because the exhibit clearly shows significantly higher false positive and false negative rates for Group B, indicating the model treats groups differently. Option A is wrong because overfitting is not indicated by these metrics. Option C is wrong because the recommendation is to fix the issue, not that the model is already fair. Option D is wrong because Group A has lower rates, suggesting Group B's data may be insufficient, but the cause is the disparity itself.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate 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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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