Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A healthcare startup wants to use generative AI to provide clinical decision support. They must minimize the risk of harmful hallucinations. Which business strategy is most appropriate?
⚠ Common exam trap
Google Cloud often tests the misconception that fine-tuning alone is sufficient for domain-specific accuracy, when in fact RAG is superior for reducing hallucinations because it provides dynamic, verifiable grounding rather than static memorization.
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 retrieval-augmented generation with meticulously curated medical literature.
Retrieval-augmented generation (RAG) grounds the model's output in a trusted, external knowledge base—here, curated medical literature—which directly reduces the risk of hallucination by forcing the model to cite or derive answers from verified sources. This is the most effective strategy for clinical decision support because it combines generative flexibility with factual accuracy, unlike methods that only limit output or rely on post-hoc filtering.
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 retrieval-augmented generation with meticulously curated medical literature.
Why this is correct
RAG uses retrieved, vetted documents to generate answers, significantly reducing hallucinations by grounding responses in authoritative sources.
- ✗
Limit the model's output length to reduce hallucination risk.
Why it's wrong here
Output length limitation does not address the underlying lack of grounding and may still produce hallucinations.
- ✗
Deploy a large general-purpose model and rely on post-processing filters.
Why it's wrong here
Post-processing filters may not catch all hallucinations, especially subtle ones.
- ✗
Use a custom fine-tuned model on a proprietary medical dataset.
Why it's wrong here
Fine-tuning on limited data may still hallucinate on edge cases and doesn't guarantee grounding in up-to-date literature.
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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.