AIP-C01 · Question #31
A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source…
The correct answer is C. Enable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3. Option C is the correct solution because it directly addresses the root cause of the problem-- overly broad retrieval--while requiring minimal architectural change. Amazon Bedrock Knowledge Bases support metadata-aware filtering, which allows the system to constrain retrieval…
Question
A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket. The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time. The company determines that the underlying models will not benefit from additional fine-tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture. Which solution will meet these requirements?
Options
- AEnhance embeddings by using a domain-adapted model that is specifically trained on emergency
- BMigrate to Amazon OpenSearch Service. Use vector fields and metadata filters to define the
- CEnable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3
- DMigrate to an Amazon Q Business index to perform structured metadata filtering and document
How the community answered
(29 responses)- A3% (1)
- B17% (5)
- C72% (21)
- D7% (2)
Explanation
Option C is the correct solution because it directly addresses the root cause of the problem-- overly broad retrieval--while requiring minimal architectural change. Amazon Bedrock Knowledge Bases support metadata-aware filtering, which allows the system to constrain retrieval queries based on indexed metadata such as content type, publication date, source, or category. By indexing Amazon S3 object metadata, the company can restrict vector searches to relevant subsets of the corpus, such as recent emergency reports, specific content formats, or trusted sources. This significantly reduces the number of documents evaluated during retrieval, which improves both latency and result relevance without changing embedding models or retrieval This approach aligns with AWS best practices for optimizing RAG systems: when embeddings are already strong, retrieval quality is often improved by narrowing the candidate set rather than increasing model complexity. Metadata filtering reduces noise and ensures that retrieved documents are more contextually aligned with user queries.
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