AWS Certified Generative AI Developer - Professional
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A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5–100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.
Which solution will meet these requirements?
A
Use a direct model invocation approach that uses Anthropic Claude to process each financial document as a single input. Use fine-tuned prompts that instruct the model to parse tables and text separately.
B
Use Amazon Bedrock Knowledge Bases to create a Retrieval Augmented Generation (RAG) application that retrieves relevant information from contextually chunked sections of financial documents. Segment documents based on their structural layout. Include citations that reference the original source materials.
C
Deploy an Amazon Bedrock agent that has an action group that calls custom AWS Lambda functions to analyze financial documents. Configure the Lambda functions to perform fixed-size chunking when a user submits a query about financial metrics.
D
Create one specialized Amazon Bedrock application that is optimized for structured data. Create a second application that is optimized for unstructured data. Configure each application to use a tailored chunking strategy that is suited to the application's content type. Implement logic to link queries to the appropriate sources.