The answer is applying fairness metrics during model evaluation, along with removing protected attributes from training data and ensuring diverse and representative training datasets. These three strategies directly address bias by first preventing the model from learning direct correlations with sensitive features like race or gender—a pre-processing technique that reduces direct discrimination, though proxy variables must still be monitored. Fairness metrics then quantitatively assess model outcomes across demographic groups during evaluation, catching disparities that raw accuracy might miss. On the Salesforce AI Associate exam, this question tests your understanding of bias mitigation as a lifecycle process, not a single fix; a common trap is assuming removing protected attributes alone suffices, ignoring that correlated proxies (e.g., zip code for race) can perpetuate bias. A useful memory tip is the "Three Ds": De-identify (remove protected attributes), Diagnose (apply fairness metrics), and Diversify (use representative data).
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.
Which THREE strategies can help mitigate bias in an AI model? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove protected attributes from training data
Option A is correct because removing protected attributes (e.g., race, gender) from training data reduces the risk of the model directly learning correlations with these sensitive features. This is a common pre-processing technique to prevent direct discrimination, though it may not eliminate indirect bias if correlated proxy features remain.
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 protected attributes from training data
Why this is correct
Removing attributes like race/gender can prevent direct discrimination.
Related concept
Read the scenario before looking for a memorised answer.
✗
Focus training on majority group data for accuracy
Why it's wrong here
This would worsen bias against minorities.
✗
Randomize a portion of model outputs
Why it's wrong here
Randomization does not address underlying bias.
✓
Use diverse and representative training data
Why this is correct
Diverse data reduces underrepresentation.
Related concept
Read the scenario before looking for a memorised answer.
✓
Apply fairness metrics during model evaluation
Why this is correct
Fairness metrics help detect and reduce bias.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that simply removing protected attributes or randomizing outputs is sufficient to eliminate bias, when in fact bias can persist through proxies and requires comprehensive fairness evaluation and diverse data.
Detailed technical explanation
How to think about this question
Bias mitigation often involves a combination of pre-processing (e.g., reweighting, data augmentation), in-processing (e.g., adversarial debiasing, fairness constraints), and post-processing (e.g., equalized odds calibration). For example, applying fairness metrics like demographic parity or equal opportunity during evaluation helps quantify disparities, enabling targeted adjustments. Real-world scenarios, such as hiring algorithms, show that even after removing protected attributes, proxy variables like zip code or education level can perpetuate bias, requiring iterative testing and threshold tuning.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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 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: Remove protected attributes from training data — Option A is correct because removing protected attributes (e.g., race, gender) from training data reduces the risk of the model directly learning correlations with these sensitive features. This is a common pre-processing technique to prevent direct discrimination, though it may not eliminate indirect bias if correlated proxy features remain.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO are best practices for mitigating bias in AI models?
hard
A.Using complex deep learning models.
✓ B.Using balanced training datasets.
C.Removing all sensitive attributes.
D.Training models on the most recent data only.
✓ E.Applying disparity analysis.
Why B: Options A and C are correct. Using balanced training datasets helps reduce representation bias, and applying disparity analysis identifies where bias exists. Option B is wrong because removing sensitive attributes alone may not eliminate proxy variables. Option D is wrong because using only recent data may still contain bias. Option E is wrong because complex deep learning models can be harder to audit and may amplify bias.
Last reviewed: Jun 30, 2026
Question Discussion
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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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