The correct action is to retrain the model with balanced training data. This directly addresses disparate impact by ensuring the model learns from a dataset where each group is proportionally represented, preventing the algorithm from overfitting to a majority class and making biased predictions. On the Salesforce AI Associate exam, this scenario tests your understanding of fairness mitigation techniques, often appearing in questions about model evaluation and ethical AI deployment. A common trap is assuming you can simply adjust a deployment ratio or raise an accuracy threshold, but neither fixes the root cause of imbalanced data. Remember the mnemonic "Bias Begone with Balanced Batches" to recall that retraining with balanced data is the first, least drastic step to reduce bias before considering model removal.
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. 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.
Exhibit
Einstein Prediction Builder model "Churn_Model_v1" has accuracy 92% but shows disparate impact on ethnic groups (Disparate Impact Ratio = 0.6).
Refer to the exhibit. What action should be taken?
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 with balanced training data
Option B is correct because retraining with balanced data can help reduce disparate impact. Option A is wrong deploying with a ratio of 0.6 is likely illegal and unethical. Option C is wrong increasing accuracy threshold does not address fairness. Option D is wrong removing the model may be too drastic without attempting mitigation.
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.
✗
Increase the accuracy threshold
Why it's wrong here
Accuracy threshold does not directly affect fairness.
✗
Remove the model
Why it's wrong here
Removal may be premature; retraining is a better first step.
✗
Deploy as is
Why it's wrong here
A ratio of 0.6 indicates significant bias; deploying would be unethical.
✓
Retrain the model with balanced training data
Why this is correct
Balancing data can reduce disparate impact.
Related concept
Read the scenario before looking for a memorised answer.
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: Retrain the model with balanced training data — Option B is correct because retraining with balanced data can help reduce disparate impact. Option A is wrong deploying with a ratio of 0.6 is likely illegal and unethical. Option C is wrong increasing accuracy threshold does not address fairness. Option D is wrong removing the model may be too drastic without attempting mitigation.
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.
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Question Discussion
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