The answer is imbalanced training data, which causes the model to perform better on the majority group. This is the most likely cause of a fairness issue because when one class or demographic group is overrepresented in the training set, the model learns to optimize for that group’s patterns, leading to disparate accuracy or error rates for the minority group. On the Salesforce AI Associate exam, this concept tests your understanding that bias often stems from data distribution problems rather than the algorithm itself—a common trap is assuming high overall accuracy means the model is fair, but imbalanced data can mask poor performance on underrepresented groups. Remember that fairness issues are frequently rooted in the data, not the model’s code. A useful memory tip: “Majority rules, minority loses”—if your training data is lopsided, your model’s performance will be too.
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
Refer to the exhibit.
```
Model: Churn Predictor v2
Training Data: 80% male, 20% female
Accuracy: 85% overall, 90% male, 60% female
Fairness Metric: Equal Opportunity Difference = 0.3
```
Refer to the exhibit. What is the most likely cause of the fairness issue?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training data is imbalanced, causing the model to perform better on the majority group.
Option B is correct because imbalanced training data often leads to disparate performance. Option A is wrong because the model is not inherently biased. Option C is wrong because overall accuracy can be high despite bias. Option D is wrong because there is no indication of overfitting.
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 overfits to the male group.
Why it's wrong here
Overfitting is not indicated by lower accuracy on minority.
✓
The training data is imbalanced, causing the model to perform better on the majority group.
Why this is correct
Imbalanced data leads to unequal performance.
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 overall accuracy is too low.
Why it's wrong here
Overall accuracy is 85%, which is acceptable.
✗
The model is inherently biased against females.
Why it's wrong here
Bias stems from data, not model itself.
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.
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 training data is imbalanced, causing the model to perform better on the majority group. — Option B is correct because imbalanced training data often leads to disparate performance. Option A is wrong because the model is not inherently biased. Option C is wrong because overall accuracy can be high despite bias. Option D is wrong because there is no indication of overfitting.
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.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.