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Generative AI Leader Fundamentals of Generative AI Practice Question

A healthcare company is building a clinical decision support system using Gemini 1.5 Pro on Vertex AI. They need responses that are highly accurate and comply with medical regulations, including traceability to source documents. They have a large corpus of curated medical guidelines stored in PDFs in Cloud Storage. Their team has experience with both fine-tuning and prompt engineering. Which approach best ensures regulatory compliance and accuracy?

⚠ Common exam trap

The Generative AI Leader exam often tests the misconception that fine-tuning is the best way to ensure accuracy and compliance for domain-specific tasks, but the trap here is that fine-tuning sacrifices traceability and can introduce staleness, whereas grounding with system instructions preserves source attribution and regulatory compliance.

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 a combination of grounding to the medical guidelines and prompt engineering with system instructions specifying compliance requirements.

Grounding the model to the curated medical guidelines in Cloud Storage ensures responses are directly traceable to source documents, which is critical for medical regulatory compliance. Combining this with system instructions that specify compliance requirements (e.g., HIPAA, FDA guidelines) enforces behavioral constraints without altering the model's weights, maintaining accuracy and auditability.

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 combination of grounding to the medical guidelines and prompt engineering with system instructions specifying compliance requirements.
Why this is correct
Grounding via Vertex AI Search retrieves passages from the curated PDF guidelines, so every response cites verifiable source documents — satisfying the traceability mandate. System instructions then enforce regulatory constraints at inference time, and because the corpus stays authoritative, accuracy improves without retraining risk. Fine-tuning cannot provide citation-level provenance.
✗
Use prompt engineering with system instructions and few-shot examples, but no grounding.
Why it's wrong here
Without grounding, Gemini 1.5 Pro answers from parametric memory, so responses cannot be traced to the curated PDF guidelines, failing the traceability requirement. Prompt engineering alone suits style and format control, but regulatory citation demands retrieval from Cloud Storage via Vertex AI Search grounding.
✗
Use grounding to the medical guidelines but rely on prompt engineering only for compliance instructions.
Why it's wrong here
Grounding supplies traceable citations to the PDF guidelines, yet compliance rules such as refusal thresholds and audit logging need deterministic enforcement, which prompt instructions alone cannot guarantee. This approach suits exploratory prototypes, not regulated clinical decisions requiring verifiable, enforced safeguards.
✗
Fine-tune the model on the medical guidelines corpus to internalize the knowledge.
Why it's wrong here
Fine-tuning embeds guideline content into the model's weights, so responses cannot cite or trace back to the specific source PDFs, breaking the traceability requirement. It is tempting because fine-tuning suits teaching a model a consistent output style or domain vocabulary, but where verifiable citation to stored documents is mandated, retrieval-grounded prompting is required.

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Generative AI Leader Fundamentals of Generative AI Practice Question

Why each option matters

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