- A
Create separate models for each demographic group to ensure accuracy.
Why wrong: This may lead to unequal treatment and is not efficient.
- B
Continuously monitor model performance across demographic groups and report disparities.
Why wrong: Monitoring is necessary but not sufficient.
- C
Retrain the model using a more diverse dataset that represents the target population.
Diverse training data improves fairness and performance.
- D
Adjust the decision threshold for minority groups to improve sensitivity.
Why wrong: Threshold adjustment does not correct the model's predictive bias.
Question 235 of 1,000
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.
A hospital uses an AI model to predict patient deterioration. The model was trained on data from a single hospital with a predominantly white patient population. When deployed at a hospital serving a diverse population, the model underperforms for minority groups. What is the most effective way to address this ethical issue?
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
✓
Retrain the model using a more diverse dataset that represents the target population.
Option C is correct because retraining the model with a diverse dataset that represents the target population directly addresses the root cause of the bias—the lack of diverse training data. This approach improves model performance for all groups, especially minorities. Option A is incorrect: creating separate models for each demographic group can be logistically complex and may lead to stigmatization, and it does not address the underlying data diversity issue. Option B is incorrect: while continuously monitoring performance disparities is important for accountability, it does not fix the existing bias in the model. Option D is incorrect: adjusting the decision threshold for minority groups might improve sensitivity for those groups but does not address the fundamental model bias caused by unrepresentative training data.
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.
- ✗
Create separate models for each demographic group to ensure accuracy.
Why it's wrong here
This may lead to unequal treatment and is not efficient.
- ✗
Continuously monitor model performance across demographic groups and report disparities.
Why it's wrong here
Monitoring is necessary but not sufficient.
- ✓
Retrain the model using a more diverse dataset that represents the target population.
Why this is correct
Diverse training data improves fairness and performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Adjust the decision threshold for minority groups to improve sensitivity.
Why it's wrong here
Threshold adjustment does not correct the model's predictive bias.
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.
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.
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Ethical Considerations of AI — study guide chapter
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FAQ
Questions learners often ask
What does this AI Associate question test?
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: Retrain the model using a more diverse dataset that represents the target population. — Option C is correct because retraining the model with a diverse dataset that represents the target population directly addresses the root cause of the bias—the lack of diverse training data. This approach improves model performance for all groups, especially minorities. Option A is incorrect: creating separate models for each demographic group can be logistically complex and may lead to stigmatization, and it does not address the underlying data diversity issue. Option B is incorrect: while continuously monitoring performance disparities is important for accountability, it does not fix the existing bias in the model. Option D is incorrect: adjusting the decision threshold for minority groups might improve sensitivity for those groups but does not address the fundamental model bias caused by unrepresentative training data.
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.
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: Jun 23, 2026
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