- A
Remove all protected attribute fields from the training dataset
Why wrong: Removing fields does not eliminate proxy attributes that may cause indirect discrimination.
- B
Rely on model accuracy metrics to ensure equal treatment
Why wrong: Accuracy alone does not measure fairness; a model can be accurate yet biased.
- C
Use the bias detection and fairness reporting built into Einstein Prediction Builder
Salesforce provides tools to detect and report bias, enabling proactive fairness assessment.
- D
Allow admins to manually override model predictions for certain groups
Why wrong: Manual override is not a scalable or transparent solution to bias.
Question 20 of 1,000
hardMultiple ChoiceObjective-mapped
AI Associate Practice Question: During an AI ethics review, a stakeholder asks…
This AI Associate practice question tests your understanding of ai associate exam topics. 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.
During an AI ethics review, a stakeholder asks how Salesforce ensures that Einstein models do not discriminate based on protected attributes. Which mechanism addresses this concern?
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
✓
Use the bias detection and fairness reporting built into Einstein Prediction Builder
Option C is correct because Salesforce Einstein Prediction Builder includes built-in bias detection and fairness reporting that automatically checks models for discrimination based on protected attributes. This feature analyzes model predictions against demographic groups and generates fairness metrics, allowing stakeholders to identify and mitigate bias directly within the platform.
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.
- ✗
Remove all protected attribute fields from the training dataset
Why it's wrong here
Removing fields does not eliminate proxy attributes that may cause indirect discrimination.
- ✗
Rely on model accuracy metrics to ensure equal treatment
Why it's wrong here
Accuracy alone does not measure fairness; a model can be accurate yet biased.
- ✓
Use the bias detection and fairness reporting built into Einstein Prediction Builder
Why this is correct
Salesforce provides tools to detect and report bias, enabling proactive fairness assessment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Allow admins to manually override model predictions for certain groups
Why it's wrong here
Manual override is not a scalable or transparent solution to bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that removing protected attributes from training data is sufficient to prevent bias, but the trap here is that proxy discrimination through correlated features remains undetected, making bias detection tools like Einstein’s fairness reporting the correct answer.
Detailed technical explanation
How to think about this question
Einstein Prediction Builder’s bias detection works by comparing prediction distributions across protected attribute values (e.g., race, gender) using statistical parity metrics such as disparate impact ratio and equal opportunity difference. Under the hood, it computes conditional probabilities of favorable outcomes for each group and flags violations of configurable fairness thresholds (e.g., a ratio below 0.8). In a real-world scenario, a lending model might show high overall accuracy but still deny loans disproportionately to a protected group due to proxy features like 'number of late payments'—the fairness report would surface this disparity even if the protected attribute itself was excluded from training.
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.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use the bias detection and fairness reporting built into Einstein Prediction Builder — Option C is correct because Salesforce Einstein Prediction Builder includes built-in bias detection and fairness reporting that automatically checks models for discrimination based on protected attributes. This feature analyzes model predictions against demographic groups and generates fairness metrics, allowing stakeholders to identify and mitigate bias directly within the platform.
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
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 30, 2026
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