Q13 — AWS AIF-C01 Ch.1

Question 13 of 100 | ← Chapter 1

A company wants to use a large language model (LLM) and Amazon Bedrock to build a chat interface for its product manuals, which are stored in PDF format. Which solution is the most cost-effective and efficient to meet these requirements?

• A. When a prompt is submitted to Amazon Bedrock, use prompt engineering to include a single PDF file as context in the user prompt.
• B. When a prompt is submitted to Amazon Bedrock, use prompt engineering to include all PDF files as context in the user prompt.
• C. Fine-tune the model using all PDF documents with Amazon Bedrock, then use the fine-tuned model to process user prompts.
• D. Upload the PDF documents to an Amazon Bedrock knowledge base. When users submit prompts to Amazon Bedrock, use the knowledge base to provide context. ✓

Correct Answer: D. Upload the PDF documents to an Amazon Bedrock knowledge base. When users submit prompts to Amazon Bedrock, use the knowledge base to provide context.

Explanation

This question assesses optimal architecture for RAG (retrieval-augmented generation) with Amazon Bedrock. Uploading PDFs to an Amazon Bedrock knowledge base enables efficient, scalable, and cost-effective retrieval of relevant context without embedding entire documents in every prompt (A, B) or expensive full-model fine-tuning (C). The knowledge base automatically handles chunking, embedding, and semantic search — reducing latency, token usage, and operational overhead. Thus, option D is the most economical and efficient solution.