Generative AI Leader Fundamentals of Generative AI Practice Question
A team is building a medical diagnosis assistant using a foundation model. To comply with regulations, they need to ensure the model does not make up facts. What is the best approach?
⚠ Common exam trap
Google Cloud often tests the misconception that reducing temperature or using a smaller model can eliminate hallucination, when in fact only grounding with external, verifiable data sources can reliably prevent fact fabrication in high-stakes domains.
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
✓
Use grounding with Vertex AI Search
Grounding with Vertex AI Search is the best approach because it connects the foundation model's outputs to a verifiable, curated knowledge base, ensuring factual accuracy and compliance with regulations that prohibit hallucination. By retrieving information from a trusted source (e.g., medical databases) in real time, the model can cite evidence and avoid generating unverified claims.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a small model to hallucinate less
Why it's wrong here
Small models also hallucinate and may lack medical knowledge.
- ✓
Use grounding with Vertex AI Search
Why this is correct
Grounding provides verifiable citations and reduces fabrication.
- ✗
Reduce temperature to 0
Why it's wrong here
Zero temperature makes outputs deterministic but does not ensure truthfulness.
- ✗
Fine-tune on medical journals
Why it's wrong here
Fine-tuning does not guarantee factuality and may still hallucinate.
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