A retail company is building an AI assistant that uses a large documentation corpus for general questions. For price-related answers, the company requires using only documents less than one month old. Which solution satisfies this requirement?

The correct solution leverages Amazon Q Business's ability to apply document attribute filters. This allows you to define a filter, such as documentcreationdate (currentdate - 1 month), specifically for price-related queries. Amazon Q Business then intelligently applies this filter during retrieval-augmented generation (RAG) to ensure only recent documents inform price answers, while general questions can access the entire corpus. Incorrect options: "Configure source attribution" in Amazon Q Business helps users understand where information comes from but doesn't filter documents based on recency for specific query types. Using Amazon Q Developer is incorrect because it's designed for developer-specific tasks like code generation and debugging, not for general AI assistant functionality with document filtering for end-user queries. Manually organizing documents into folders and granting access is a cumbersome and inefficient approach that doesn't scale and doesn't utilize the advanced filtering capabilities of services like Amazon Q Business.