- A
Show only the most popular products to all customers regardless of browsing history.
Why wrong: This removes personalization and reduces relevance, likely hurting conversion rates.
- B
Discontinue the AI recommendation system entirely.
Why wrong: Discontinuing eliminates revenue benefits and may disappoint customers.
- C
Keep the current system as it maximizes revenue.
Why wrong: Maximizing revenue at the cost of fairness can harm brand reputation and customer trust.
- D
Implement fairness constraints to ensure similar recommendation distributions across demographic groups.
Fairness constraints balance business goals with ethical considerations, maintaining personalization while avoiding bias.
Question 747 of 1,000
Bias Mitigation in AI Models with Fairness Constraints
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.
A retail company uses an AI recommendation engine to suggest products to online shoppers. The engine uses past purchase history and browsing behavior. Recently, a customer advocacy group publishes a report showing that the engine recommends higher-priced products to customers in affluent zip codes and lower-priced products to customers in lower-income areas, even when both groups have similar browsing histories. The company's revenue has increased since implementing the engine, and marketing teams are pleased. However, the company wants to maintain a reputation for fairness. Which action should the company take?
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
✓
Implement fairness constraints to ensure similar recommendation distributions across demographic groups.
Option D is correct because implementing fairness constraints directly addresses the biased recommendation patterns without eliminating the benefits of personalization. This allows the company to maintain revenue while promoting equitable outcomes. Option A reduces personalization and may still not ensure fairness. Option B is too extreme and would lose all AI advantages. Option C ignores the ethical concern entirely.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Show only the most popular products to all customers regardless of browsing history.
Why it's wrong here
This removes personalization and reduces relevance, likely hurting conversion rates.
- ✗
Discontinue the AI recommendation system entirely.
Why it's wrong here
Discontinuing eliminates revenue benefits and may disappoint customers.
- ✗
Keep the current system as it maximizes revenue.
Why it's wrong here
Maximizing revenue at the cost of fairness can harm brand reputation and customer trust.
- ✓
Implement fairness constraints to ensure similar recommendation distributions across demographic groups.
Why this is correct
Fairness constraints balance business goals with ethical considerations, maintaining personalization while avoiding bias.
Related concept
OSPF neighbours must agree on key parameters.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Visual reference
What to study next
Got this wrong? Here's your next step.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related AI Associate OSPF questions on adjacency and route selection.
- →
Ethical Considerations of AI — study guide chapter
Learn the concepts, then practise the questions
- →
Ethical Considerations of AI practice questions
Targeted practice on this topic area only
- →
All AI Associate questions
1,000 questions across all exam domains
- →
Salesforce AI Associate AI Associate study guide
Full concept coverage aligned to exam objectives
- →
AI Associate practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI Associate practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Ethical AI and Data Privacy practice questions
Practise AI Associate questions linked to Ethical AI and Data Privacy.
Salesforce Einstein AI Features practice questions
Practise AI Associate questions linked to Salesforce Einstein AI Features.
AI Fundamentals practice questions
Practise AI Associate questions linked to AI Fundamentals.
AI Capabilities in CRM practice questions
Practise AI Associate questions linked to AI Capabilities in CRM.
Ethical Considerations of AI practice questions
Practise AI Associate questions linked to Ethical Considerations of AI.
Data for AI practice questions
Practise AI Associate questions linked to Data for AI.
AI Associate fundamentals practice questions
Practise AI Associate questions linked to AI Associate fundamentals.
AI Associate scenario practice questions
Practise AI Associate questions linked to AI Associate scenario.
AI Associate troubleshooting practice questions
Practise AI Associate questions linked to AI Associate troubleshooting.
Practice this exam
Start a free AI Associate practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Implement fairness constraints to ensure similar recommendation distributions across demographic groups. — Option D is correct because implementing fairness constraints directly addresses the biased recommendation patterns without eliminating the benefits of personalization. This allows the company to maintain revenue while promoting equitable outcomes. Option A reduces personalization and may still not ensure fairness. Option B is too extreme and would lose all AI advantages. Option C ignores the ethical concern entirely.
What should I do if I get this AI Associate question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related AI Associate OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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 →
Same concept, more angles
2 more ways this is tested on AI Associate
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. A company uses Einstein's predictive lead scoring. The model inadvertently overweights leads from certain geographic regions. Which action aligns with Salesforce's Responsible AI principles?
hard- ✓ A.Implement a feedback loop to continuously monitor and adjust.
- B.Remove the geographic feature from the model.
- C.Use the model as-is because it improves overall accuracy.
- D.Only use the model for regions where it performs well.
Why A: Option A is correct because continuously monitoring and adjusting the model aligns with Salesforce's Responsible AI principles, which emphasize ongoing oversight to mitigate bias. Option B is incorrect because simply removing the geographic feature may not eliminate proxy variables and could reduce model accuracy. Option C is incorrect because using a biased model as-is is unethical and violates responsible AI practices. Option D is incorrect because restricting the model's usage does not address the root cause of bias and may lead to inequitable outcomes.
Variation 2. A retail company uses Einstein to personalize product recommendations. The AI model is trained on customer purchase data that includes sensitive attributes like race and gender. The company wants to ensure ethical use. Which action would best address fairness concerns?
hard- ✓ A.Remove race and gender fields from the training dataset
- B.Obtain explicit consent from customers for data use
- C.Add more demographic data to improve model accuracy
- D.Randomize recommendations to ensure equal treatment
Why A: The correct answer is A because removing sensitive attributes like race and gender from the training dataset helps prevent the model from directly using these factors to discriminate, thereby reducing fairness concerns. Option B is incorrect because obtaining consent does not address bias; it only addresses privacy. Option C is incorrect because adding more demographic data could amplify bias if that data is correlated with sensitive attributes. Option D is incorrect because randomizing recommendations sacrifices personalization and does not correct underlying bias in the model.
Keep practising
More AI Associate practice questions
- An admin wants to compare the AI-generated forecast with a rep's commit forecast to identify gaps. Which feature should…
- A Salesforce admin implements Einstein Bots for customer service. To ensure the bot does not use biased language, what s…
- Which Einstein feature provides automated statistical analysis of Salesforce data, including story creation and improvem…
- A sales operations team wants to improve forecast accuracy by using AI. They currently use manual rollups. Which TWO Ein…
- A sales rep wants to generate a personalized email to a prospect using AI. Which Einstein GPT feature should they use?
- A healthcare company uses Einstein Prediction Builder to predict patient no-shows. After training a model, they receive…
Last reviewed: Jun 23, 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.
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.