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AI-102 Plan and manage an Azure AI solution Practice Question

Which TWO actions should you take when designing an Azure AI solution that uses Microsoft Foundry to ensure responsible AI practices?

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 a human-in-the-loop review for critical decisions

Implementing a human-in-the-loop review provides oversight for critical decisions. Option C is correct because running an AI fairness assessment detects bias. Option B is wrong because optimizing for maximum throughput is about performance, not responsible AI. Option D is wrong because storing all training data indefinitely may violate privacy and is not a recommended practice for responsible AI. Option E is wrong because removing all explainability metrics hinders transparency, which is important for responsible AI.

Answer analysis

Option-by-option breakdown

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.
✗
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.
✗
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.

Go deeper

AI-102 Plan and manage an Azure AI solution Practice Question

Why each option matters

Option-by-option breakdown

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