Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A hospital wants to build a generative AI assistant that answers clinician questions using the hospital's own clinical guidelines. The guidelines change frequently, and the hospital wants the assistant to cite the exact guideline section used. Which approach best meets these requirements while minimizing model retraining?
⚠ Common exam trap
The trap here is assuming fine-tuning is the way to add domain knowledge; when content changes often and citations are required, retrieval grounding is the appropriate pattern, not weight updates.
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
✓
Ground the assistant on the guidelines using retrieval-augmented generation with Vertex AI Search
Grounding with retrieval-augmented generation separates knowledge from the model: guideline passages are indexed and retrieved per query, so updates mean re-indexing rather than retraining, and responses can cite the retrieved section. Fine-tuning forces retraining on every change and gives no citations, temperature affects randomness not accuracy, and stuffing all guidelines into the prompt hits context limits without reliable attribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ground the assistant on the guidelines using retrieval-augmented generation with Vertex AI Search
Why this is correct
Retrieval-augmented generation retrieves relevant guideline passages at query time and grounds the model's answer in them, so citations can point to the exact section. Because the guidelines live in an index rather than the model weights, updates require re-indexing documents, not retraining, which minimizes retraining effort while keeping answers current and attributable.
- ✗
Expand the model's context window and paste all guidelines into every prompt
Why it's wrong here
Pasting the entire guideline corpus into each prompt is limited by context window size, raises cost and latency, and still offers no reliable citation of the specific section used. It also becomes unwieldy as the corpus grows, so it does not scale to frequently updated clinical guidelines.
- ✗
Fine-tune the Gemini model every time the clinical guidelines are updated
Why it's wrong here
Fine-tuning bakes knowledge into model weights, so every guideline change would require a new training run, evaluation, and redeployment. That is expensive, slow, and does not naturally produce citations to specific sections. It directly contradicts the goal of minimizing retraining and keeping a frequently changing corpus current.
- ✗
Increase the model's temperature so it recalls guideline details more accurately
Why it's wrong here
Temperature controls randomness in token selection; raising it makes output more varied and less deterministic, which harms factual reliability rather than improving recall of guideline details. It provides no mechanism for citing sources, so it fails both the accuracy and the citation requirements of this clinical scenario.
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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
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