Quick Answer
The answer is fairness. When an AI tool recommends high-commission products over what best fits the customer, it violates the ethical principle of fairness because the recommendations are biased toward the seller’s profit rather than the customer’s genuine needs. Fairness in AI ethics requires that outcomes are equitable and serve all stakeholders impartially, not just the party with the most to gain financially. On the Salesforce AI Associate exam, this scenario tests your understanding of how bias in recommendation systems undermines trust and ethical AI deployment. A common trap is to confuse this with transparency, but transparency concerns openness about how decisions are made, not the bias of the decisions themselves. Remember the memory tip: “Fairness puts the customer first, not the commission.”
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.
A sales team uses an AI tool to recommend products to customers. The tool recommends high-commission products over what best fits the customer. Which ethical principle is being violated?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Privacy
Why wrong: Privacy pertains to data use, not the nature of recommendations.
Transparency
Why wrong: Transparency is about explaining how decisions are made, but the core issue is unfairness.
Fairness
Recommending based on commission rather than customer need is unfair.
Accountability
Why wrong: Accountability is about who is responsible, but the direct violation is fairness.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Fairness
Option B is correct because fairness requires that recommendations benefit the customer, not just the seller. Option A is wrong as transparency is about openness, not directly violated here. Option C is wrong because accountability involves responsibility, which may also be an issue but fairness is primary. Option D is wrong because privacy is about data protection.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Privacy
Why it's wrong here
Privacy pertains to data use, not the nature of recommendations.
Transparency
Why it's wrong here
Transparency is about explaining how decisions are made, but the core issue is unfairness.
Fairness
Why this is correct
Recommending based on commission rather than customer need is unfair.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Accountability
Why it's wrong here
Accountability is about who is responsible, but the direct violation is fairness.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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FAQ
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
The correct answer is: Fairness — Option B is correct because fairness requires that recommendations benefit the customer, not just the seller. Option A is wrong as transparency is about openness, not directly violated here. Option C is wrong because accountability involves responsibility, which may also be an issue but fairness is primary. Option D is wrong because privacy is about data protection.
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.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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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.
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