- A
Use only historical data from the past six months
Why wrong: Short time windows may not capture enough data for robust model training and could introduce seasonality bias.
- B
Validate model accuracy separately for different patient subgroups
Subgroup validation ensures consistent accuracy across populations.
- C
Exclude all demographic features from the model to avoid bias
Data minimisation reduces the risk of bias from sensitive attributes.
- D
Regularly audit the model for bias across demographic groups
Bias audits identify unfair predictions.
- E
Include as many features as possible to maximize predictive power
Why wrong: Including too many features, especially sensitive ones, can introduce bias and reduce interpretability.
Question 560 of 1,000
AI Associate Ethical AI and Data Privacy Practice Question
This AI Associate practice question tests your understanding of ethical ai and data privacy. 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.
A healthcare provider is using Einstein to predict patient readmission risks. They must ensure the model is both accurate and fair. Which THREE actions should they take? (Choose 3)
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
✓
Validate model accuracy separately for different patient subgroups
Option B is correct because validating model accuracy separately for different patient subgroups is a key fairness practice. It ensures the model performs consistently across diverse populations, preventing hidden disparities that could lead to biased clinical decisions. This aligns with responsible AI principles that require subgroup performance analysis to detect and mitigate algorithmic bias.
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.
- ✗
Use only historical data from the past six months
Why it's wrong here
Short time windows may not capture enough data for robust model training and could introduce seasonality bias.
- ✓
Validate model accuracy separately for different patient subgroups
Why this is correct
Subgroup validation ensures consistent accuracy across populations.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Exclude all demographic features from the model to avoid bias
Why this is correct
Data minimisation reduces the risk of bias from sensitive attributes.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Regularly audit the model for bias across demographic groups
Why this is correct
Bias audits identify unfair predictions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Include as many features as possible to maximize predictive power
Why it's wrong here
Including too many features, especially sensitive ones, can introduce bias and reduce interpretability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that simply excluding demographic features (Option C) guarantees fairness, when in reality, proxy variables like zip code or income can still encode bias, making regular auditing (Option D) and subgroup validation (Option B) essential.
Detailed technical explanation
How to think about this question
Under the hood, fairness validation often involves computing metrics like demographic parity, equal opportunity, or equalized odds across subgroups. For example, a model might achieve high overall AUC but show significantly lower recall for minority patient groups, leading to unequal care. Real-world scenarios, such as the widely studied COMPAS recidivism model, demonstrate how ignoring subgroup performance can perpetuate systemic bias even when aggregate accuracy appears acceptable.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Ethical AI and Data Privacy — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Validate model accuracy separately for different patient subgroups — Option B is correct because validating model accuracy separately for different patient subgroups is a key fairness practice. It ensures the model performs consistently across diverse populations, preventing hidden disparities that could lead to biased clinical decisions. This aligns with responsible AI principles that require subgroup performance analysis to detect and mitigate algorithmic bias.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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