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.
Diverse and representative training data is critical for responsible AI in medical diagnosis. If the data lacks diversity, the model may exhibit bias, leading to inaccurate or harmful diagnoses for underrepresented groups. This directly impacts fairness, safety, and regulatory compliance in healthcare 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.
- ✓
Ensure the training data is diverse and representative.
Why this is correct
Diverse data reduces bias.
- ✗
Maximize model throughput to handle high volumes.
Why it's wrong here
Throughput is secondary to safety.
- ✓
Implement human oversight for all diagnostic suggestions.
Why this is correct
Human in the loop ensures safety.
- ✓
Provide clear disclaimers about the model's limitations.
Why this is correct
Transparency is essential for responsible AI.
- ✗
Use the cheapest model to reduce costs.
Why it's wrong here
Cost should not compromise quality or safety.
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