- A
Test the model for bias across different product types and lighting conditions
Bias testing ensures fair performance.
- B
Keep the model's decision-making process proprietary to protect intellectual property
Why wrong: Secrecy hinders transparency and trust.
- C
Deploy the model without human oversight to maximize efficiency
Why wrong: Lack of human oversight reduces accountability.
- D
Provide clear documentation on the model's limitations and expected accuracy
Documentation supports transparency.
- E
Only use training images from a single supplier to maintain consistency
Why wrong: Limiting data source may introduce bias.
Question 818 of 1,000
Ethical AI Practices: Bias Testing and Documentation
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 company is deploying Einstein Vision for product quality inspection. To ensure ethical use, which TWO practices should they adopt? (Choose two.)
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
✓
Test the model for bias across different product types and lighting conditions
Option A is correct because testing for bias across different product types and lighting conditions helps ensure the model treats all products fairly. Option D is correct because providing clear documentation on the model's limitations and expected accuracy promotes transparency and informed decision-making. Option B (keeping the model's decision-making proprietary) violates transparency, which is a key ethical principle. Option C (deploying without human oversight) undermines accountability and could lead to unchecked errors. Option E (using training images from a single supplier) may introduce bias, reducing fairness and reliability.
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.
- ✓
Test the model for bias across different product types and lighting conditions
Why this is correct
Bias testing ensures fair performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keep the model's decision-making process proprietary to protect intellectual property
Why it's wrong here
Secrecy hinders transparency and trust.
- ✗
Deploy the model without human oversight to maximize efficiency
Why it's wrong here
Lack of human oversight reduces accountability.
- ✓
Provide clear documentation on the model's limitations and expected accuracy
Why this is correct
Documentation supports transparency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Only use training images from a single supplier to maintain consistency
Why it's wrong here
Limiting data source may introduce bias.
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.
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Ethical Considerations of AI — study guide chapter
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FAQ
Questions learners often ask
What does this AI Associate question test?
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: Test the model for bias across different product types and lighting conditions — Option A is correct because testing for bias across different product types and lighting conditions helps ensure the model treats all products fairly. Option D is correct because providing clear documentation on the model's limitations and expected accuracy promotes transparency and informed decision-making. Option B (keeping the model's decision-making proprietary) violates transparency, which is a key ethical principle. Option C (deploying without human oversight) undermines accountability and could lead to unchecked errors. Option E (using training images from a single supplier) may introduce bias, reducing fairness and reliability.
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.
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 wants to deploy an AI system that makes hiring decisions. To comply with ethical guidelines, what should they do before deployment?
easy- ✓ A.Conduct an ethics review and perform bias testing on diverse datasets.
- B.Ensure the system achieves high accuracy and ignore other metrics.
- C.Deploy immediately and monitor for issues.
- D.Test the system only on a small dataset to expedite launch.
Why A: Option A is correct because conducting an ethics review and performing bias testing on diverse datasets are essential steps to identify and mitigate potential discriminatory outcomes in AI-driven hiring systems. This aligns with ethical AI frameworks that require fairness, accountability, and transparency before deployment, ensuring the model does not perpetuate historical biases or violate anti-discrimination laws.
Variation 2. A company is deploying an AI system that makes recommendations to users. To ensure ethical use, they should:
hard- ✓ A.Allow users to opt out and understand how decisions are made.
- B.Make recommendations without oversight.
- C.Maximize engagement regardless of user well-being.
- D.Use only internal data.
Why A: Option A is correct because allowing users to opt out and understanding decisions respects autonomy and transparency. Option B is wrong because making recommendations without oversight can lead to harmful outcomes. Option C is wrong because maximizing engagement without regard to user well-being is unethical. Option D is wrong because using only internal data may not be sufficient and could raise privacy concerns.
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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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