AIP-C01 · Question #41
A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream
The correct answer is D. Use the latest Amazon reranker model through the reranking configuration within Amazon. Option D is the correct solution because Amazon Bedrock Knowledge Bases natively support reranking by using Amazon-managed reranker models, which are specifically designed to improve contextual relevance after the initial vector retrieval step. This approach directly addresses th
Question
A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates. The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents. Which solution will meet this requirement with the LEAST operational overhead?
Options
- ADeploy an Amazon SageMaker endpoint to run a fine-tuned ranking model. Use an Amazon API
- BUse Amazon Comprehend to classify documents and apply relevance scores. Integrate the RAG
- CImplement a retrieval pipeline that uses the Amazon Bedrock Knowledge Bases Retrieve API to
- DUse the latest Amazon reranker model through the reranking configuration within Amazon
How the community answered
(50 responses)- A14% (7)
- B6% (3)
- C4% (2)
- D76% (38)
Explanation
Option D is the correct solution because Amazon Bedrock Knowledge Bases natively support reranking by using Amazon-managed reranker models, which are specifically designed to improve contextual relevance after the initial vector retrieval step. This approach directly addresses the root cause of the issue: semantically similar but contextually irrelevant documents being passed to the foundation model. By enabling the reranking configuration within Amazon Bedrock Knowledge Bases, the application can automatically reorder retrieved documents based on deeper contextual understanding, such as regulatory scope, legal applicability, and semantic intent. This significantly improves retrieval precision, which reduces hallucinations and improves the factual accuracy of generated regulatory guidance. Option D requires no additional infrastructure, no custom orchestration logic, and no separate model hosting. The reranking is fully managed by Amazon Bedrock and integrates seamlessly with the existing RetrieveAndGenerateStream workflow. This makes it the lowest operational overhead solution.
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