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Generative AI Leader Fundamentals of Generative AI Practice Question

A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

⚠ Common exam trap

Google Cloud often tests the distinction between operational metrics (like throughput or cost) and ethical/regulatory requirements (like fairness, transparency, and human oversight) in responsible AI, leading candidates to mistakenly select performance-based options as critical considerations.

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

✓

Ensure the training data is diverse and representative.

Option A is correct because diverse and representative training data is essential to reduce bias and ensure the model performs equitably across different patient demographics, which is a core responsible AI principle in medical contexts. Option C is correct because human oversight for all diagnostic suggestions ensures that qualified clinicians review and validate AI outputs, maintaining accountability and preventing harm from erroneous predictions. Option D is correct because clear disclaimers about the model's limitations inform users of its scope and uncertainty, preventing over-reliance and supporting informed decision-making. Option B is not correct because maximizing throughput addresses performance and scalability, not responsible AI concerns such as fairness, safety, or transparency. Option E is not correct because choosing the cheapest model prioritizes cost over accuracy, safety, and ethical considerations, which is inappropriate for medical diagnosis support.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Ensure the training data is diverse and representative.
Why this is correct
Diverse, representative training data reduces demographic bias, preventing skewed diagnostic suggestions for under-represented patient groups. This satisfies the responsible AI requirement by addressing fairness at the data layer, where bias originates before model training begins.
✗
Maximize model throughput to handle high volumes.
Why it's wrong here
Throughput is an operational performance metric; it says nothing about bias, explainability, clinician oversight or patient safety, which responsible AI for diagnosis demands. It is tempting because high volume is a real deployment concern, and it would be the right answer for a capacity-planning question rather than an ethics one.
✓
Implement human oversight for all diagnostic suggestions.
Why this is correct
Human oversight ensures a qualified clinician reviews every AI diagnostic suggestion before it affects patient care, catching errors and preserving clinical accountability. This satisfies the responsible AI requirement for meaningful human control in high-stakes medical decisions.
✓
Provide clear disclaimers about the model's limitations.
Why this is correct
Clear disclaimers communicate that model outputs are decision support, not confirmed diagnoses, so clinicians and patients interpret suggestions with appropriate caution. This satisfies the responsible AI requirement for transparency about capability limits in medical contexts.
✗
Use the cheapest model to reduce costs.
Why it's wrong here
Cost minimisation optimises spend, not fairness, transparency or clinical safety, and a cheaper model may be less accurate for diagnosis. It is tempting because budget control is a legitimate engineering goal, and it would be correct if the question asked how to reduce inference cost at scale.

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Generative AI Leader Fundamentals of Generative AI Practice Question

Why each option matters

Option-by-option breakdown

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