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.
A company wants to deploy an AI system that makes hiring decisions. To comply with ethical guidelines, what should they do before deployment?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Conduct an ethics review and perform bias testing on diverse datasets.
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.
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.
✓
Conduct an ethics review and perform bias testing on diverse datasets.
Why this is correct
Ethics review and bias testing are proactive measures.
Related concept
Read the scenario before looking for a memorised answer.
✗
Ensure the system achieves high accuracy and ignore other metrics.
Why it's wrong here
Accuracy alone is insufficient for ethical AI.
✗
Deploy immediately and monitor for issues.
Why it's wrong here
Deployment without prior testing may cause harm.
✗
Test the system only on a small dataset to expedite launch.
Why it's wrong here
Small testing may not reveal biases.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that high accuracy alone guarantees ethical AI, when in fact fairness metrics and bias testing are mandatory to prevent discriminatory outcomes in high-stakes applications like hiring.
Detailed technical explanation
How to think about this question
Bias testing often involves evaluating metrics such as demographic parity, equal opportunity, and predictive parity across subgroups defined by protected attributes (e.g., race, gender). Techniques like adversarial debiasing or reweighing training samples can be applied, but they require careful validation on representative datasets that reflect the real-world population distribution. In practice, an ethics review also examines the entire ML pipeline—from data collection to model deployment—to ensure compliance with regulations like the EU AI Act or EEOC guidelines.
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: Conduct an ethics review and perform bias testing on diverse datasets. — 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.
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.
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Question Discussion
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