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Q21 Multiple choice

An ecommerce company is using an Anthropic Claude Sonnet model in Amazon Bedrock to generate product recommendations. An AWS Lambda function retrieves customer purchase data from Amazon DynamoDB, product reviews from Amazon S3, and customer profile information from Amazon RDS. Then the function sends the data directly to the Amazon Bedrock model through API calls. Recently, customers who have extensive purchase histories have begun to receive incomplete recommendations.

Amazon CloudWatch logs for the Lambda function show execution timeouts. CloudWatch logs for Amazon Bedrock API calls show intermittent errors. The company reviews the logs and finds that some requests are failing with context-length-exceeded errors. Other requests finish but appear to ignore portions of the input data.

The company wants the recommendation system to consider all customer data when the system generates recommendations. The company wants to use Amazon Bedrock Knowledge Bases to improve data organization and retrieval.

Which combination of solutions will meet these requirements? (Select TWO.)

Select all that apply.

A
Implement a chunking strategy that divides the customer data into smaller segments. Configure the model to process each segment separately. Invoke the model a final time to synthesize the individual responses into comprehensive recommendations.
B
Modify the prompt structure to place the most critical information at the beginning and end of the context window. Implement token-counting logic to truncate less important data when the interaction approaches the model's maximum context length.
C
Replace Claude Sonnet with a model that has a larger context-window capacity. Increase the Lambda function timeout to accommodate longer processing times for larger inputs.
D
Configure the recommendation system to use the Converse API. Modify the additionalModelRequestFields parameter to increase the maximum token limit beyond the model's default context-window size.
E
Implement RAG by using a knowledge base to index the customer data with vector embeddings. Retrieve only the most semantically relevant information for each recommendation request based on the current customer context.

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