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AIF-C01 Practice Question: A healthcare company is building a medical…
A healthcare company is building a medical diagnosis assistant using Amazon Bedrock. They need to ensure the model’s responses are based on the latest medical research and do not include outdated information. The company also wants to minimize costs. Which TWO actions should they take? (Select TWO)
⚠ Common exam trap
The trap here is that candidates sometimes assume they need a large model like Claude or Titan Text Express for clinical accuracy, but with RAG, a smaller model like Amazon Titan Text Lite can generate reliable responses using retrieved data, reducing costs. Also, fine-tuning is expensive and not needed if the model uses up-to-date external sources.
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 smaller model like Amazon Titan Text Lite to reduce inference costs
Option B is correct because Amazon Titan Text Lite is a smaller, lower-cost model, so using it for inference directly reduces per-token costs, which aligns with the company's goal to minimize costs. Option C is correct because Retrieval Augmented Generation (RAG) retrieves relevant passages from an up-to-date vector store of the latest medical journals and injects them into the prompt, grounding responses in current research and avoiding outdated information without retraining the model. Option A is not correct because the Converse API only manages conversation history and does not by itself ensure access to the latest medical research or reduce costs. Option D is not correct because monthly fine-tuning of a large model is expensive, slow, and risks stale knowledge between training cycles, conflicting with the cost-minimization goal. Option E is not correct because stuffing all research into a large context window increases token usage and cost and does not guarantee retrieval of the most current, relevant information.
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 the Converse API to maintain conversation history
Why it's wrong here
Converse API preserves dialogue turns, not factual currency; it cannot inject new research or reduce inference cost. It is tempting because conversation history is genuinely needed for multi-turn assistants, but retrieval augmentation is what grounds answers in current medical literature.
- ✓
Use a smaller model like Amazon Titan Text Lite to reduce inference costs
Why this is correct
Amazon Titan Text Lite cuts inference cost per token, satisfying the stated cost-minimisation constraint. However, a smaller model alone cannot ground responses in current medical research, so it must be paired with Retrieval Augmented Generation against an updated knowledge base to prevent outdated output.
- ✓
Implement RAG by indexing the latest medical journals in a vector store
Why this is correct
RAG retrieves current medical journals from a vector store and injects them into the prompt, grounding responses in the latest research rather than stale training data. This satisfies the freshness requirement without retraining the model.
- ✗
Fine-tune a large model on the latest medical data monthly
Why it's wrong here
Monthly fine-tuning is costly and slow, and it bakes research into weights that age between runs, so responses still drift out of date. Fine-tuning suits fixed style or domain tone, not continuously refreshed factual grounding; Bedrock Knowledge Bases with retrieval would inject current research at query time instead.
- ✗
Choose a model with the largest context window to include all research in the prompt
Why it's wrong here
A larger context window raises per-request token costs and still requires manually pasting research, which cannot scale to a full medical corpus. Long context suits one-off analysis of a bounded document set; retrieval over an indexed knowledge base supplies current sources at lower cost per query.
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