Generative AI Leader Practice Question: A healthcare organization wants to use generative…

Answer analysis

Option-by-option breakdown

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

• ✗

  Select a model from Model Garden that is pre-trained on medical data

  Why it's wrong here

  A medical pre-trained model is likely to generate clinical guidance rather than refuse it, directly contradicting the requirement to never give medical advice. It is tempting because domain pre-training improves clinical accuracy. System instructions and safety filters at inference time are what suppress advice and append the disclaimer.

• ✗

  Fine-tune the model with a dataset that only includes disclaimers

  Why it's wrong here

  Fine-tuning adjusts model weights toward a data distribution; it cannot guarantee every response contains a disclaimer, and a disclaimer-only dataset teaches nothing about withholding medical advice. It is tempting because fine-tuning shapes tone and format. Inference-time system instructions or output filters enforce the mandatory disclaimer reliably.

• ✗

  Use Grounding with Google Search to restrict knowledge to approved sources

  Why it's wrong here

  Grounding with Google Search constrains factual sourcing but does not enforce output behaviour, so the model can still produce medical advice or omit the disclaimer. It is tempting because grounding genuinely reduces hallucination for factual queries. System instructions or safety filters, applied at inference time, are what enforce those output constraints.

• ✓

  Configure safety settings and system instructions in the Vertex AI API call

  Why this is correct

  System instructions define persistent behavioural guardrails, and safety settings filter prohibited content at the model layer. Enforcing both in the Vertex AI API call ensures every response omits medical advice and appends the disclaimer, regardless of caller.

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