easyMultiple Choice
Generative AI Leader Practice Question: A healthcare startup is developing a generative…
A healthcare startup is developing a generative AI system to assist doctors in diagnosing rare diseases. According to Google's AI Principles, what is the MOST important requirement before deployment?
⚠ Common exam trap
The trap is assuming that a quantitative metric like 99% accuracy is the most important requirement, when Google's AI Principles prioritize human oversight and accountability over raw performance metrics in high-stakes applications.
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
✓
The system must include a mechanism for human review of all diagnostic suggestions
Google's AI Principles emphasize that AI systems should be socially beneficial and avoid creating or reinforcing unfair bias, and for high-stakes domains like healthcare, human oversight is critical. The principle of 'be accountable to people' requires that AI systems include mechanisms for human review and feedback, especially for diagnostic suggestions that could affect patient safety.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model must achieve at least 99% accuracy on a held-out test set
Why it's wrong here
A 99% accuracy target addresses statistical performance, not Google's AI Principles, which require avoiding unjust bias and ensuring safety in high-stakes medical use. Accuracy metrics alone cannot detect harm to underrepresented patient groups. Threshold-based validation would suit benchmark comparisons or model selection, where a defined performance bar governs release decisions.
- ✗
The model must be trained on the most recent medical literature
Why it's wrong here
Training on recent literature addresses currency, not the ethical deployment bar Google's AI Principles impose on high-stakes medical use. The Principles require safety testing, bias evaluation and human oversight before release. Literature recency would be the right focus for maintaining clinical accuracy in a continuously updated diagnostic tool after that governance gate is cleared.
- ✗
The startup must publish the model's architecture in a peer-reviewed journal
Why it's wrong here
Publishing architecture in a peer-reviewed journal addresses transparency for scientific scrutiny, not the safety requirement Google's AI Principles impose on high-stakes medical deployment. The Principles demand rigorous testing and human oversight before clinical use. Peer review would suit validating a research methodology, but it neither validates diagnostic safety nor satisfies the deployment gate.
- ✓
The system must include a mechanism for human review of all diagnostic suggestions
Why this is correct
Google's AI Principles require human oversight for high-stakes domains such as healthcare diagnosis. Retaining a clinician to review every diagnostic suggestion satisfies this, since the system only assists rather than determines care, keeping accountability with a qualified professional.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.