Quick Answer
The answer is to run an AI fairness assessment on the model and implement a human-in-the-loop review. A fairness assessment systematically detects bias in model predictions across sensitive attributes like race or gender, ensuring equitable outcomes, while human-in-the-loop oversight provides critical accountability by allowing domain experts to validate or override automated decisions. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of Microsoft Foundry’s responsible AI dashboard and the distinction between ethical safeguards and operational metrics. A common trap is confusing performance optimization with responsible practices—remember that accuracy alone does not guarantee fairness. Another pitfall is assuming indefinite data storage is acceptable, which violates privacy principles. For a memory tip, think “Fairness and Feedback”: fairness assessment catches bias, and human feedback catches edge cases.
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 TWO actions should you take when designing an Azure AI solution that uses Microsoft Foundry to ensure responsible AI practices?
Implement a human-in-the-loop review for critical decisions
Ensures oversight.
Optimize the model for maximum throughput
Why wrong: Not a responsible AI practice.
Run an AI fairness assessment on the model
Identifies bias.
Store all training data indefinitely for auditability
Why wrong: May violate data retention policies.
Remove all explainability metrics to simplify the model
Why wrong: Explainability is key for responsibility.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Implement a human-in-the-loop review for critical decisions
Option A and C are correct. Option A is correct because an AI fairness assessment detects bias. Option C is correct because a human-in-the-loop review provides oversight. Option B is wrong because storing all data indefinitely violates privacy. Option D is wrong because performance is not a responsible AI practice. Option E is wrong because removing all metrics hinders transparency.
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.
Implement a human-in-the-loop review for critical decisions
Why this is correct
Ensures oversight.
Related concept
Read the scenario before looking for a memorised answer.
Optimize the model for maximum throughput
Why it's wrong here
Not a responsible AI practice.
Run an AI fairness assessment on the model
Why this is correct
Identifies bias.
Related concept
Read the scenario before looking for a memorised answer.
Store all training data indefinitely for auditability
Why it's wrong here
May violate data retention policies.
Remove all explainability metrics to simplify the model
Why it's wrong here
Explainability is key for responsibility.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Identify which AI-102 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
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
The correct answer is: Implement a human-in-the-loop review for critical decisions — Option A and C are correct. Option A is correct because an AI fairness assessment detects bias. Option C is correct because a human-in-the-loop review provides oversight. Option B is wrong because storing all data indefinitely violates privacy. Option D is wrong because performance is not a responsible AI practice. Option E is wrong because removing all metrics hinders transparency.
Identify which AI-102 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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This AI-102 practice question is part of Courseiva's free Microsoft 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-102 exam.
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