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A company is using Amazon Bedrock to build an AI assistant to help internal teams...

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A company is using Amazon Bedrock to build an AI assistant to help internal teams analyze unstructured customer feedback data. The company stores the customer feedback in an Amazon S3 bucket. The S3 bucket contains more than 25 TB of historical data from mobile app reviews, chat conversations, and call center transcripts. The company expects the data source to grow by 3 GB every day. The data entries often contain multiple unrelated topics within the same input.

The company needs a solution that reliably delivers accurate answers to questions based on the data source. The solution must not export any personally identifiable information (PII) to the Amazon Bedrock model during processing or response generation.

Which solution will meet these requirements with the LEAST operational overhead?

A.

Configure an AWS Lambda function that processes each new file in the S3 bucket to detect and remove PII by using Amazon Comprehend. Configure the function to generate fixed-size chunk embeddings and store them in an Amazon OpenSearch Serverless vector store. Configure a second Lambda function to process questions, retrieve context, and invoke an Amazon Bedrock foundation model directly to generate answers.

B.

Configure an Amazon Bedrock knowledge base that synchronizes with the S3 bucket by using fixed-size chunking. Configure the knowledge base to use an Amazon Aurora PostgreSQL vector store. Configure an Amazon Bedrock guardrail to block all types of PII during input and output processing. Configure Amazon Bedrock AgentCore to use the knowledge base and the guardrail to process and answer queries.

C.

Configure an Amazon Bedrock knowledge base that synchronizes with the S3 bucket by using semantic chunking. Configure the knowledge base to use an Amazon OpenSearch Serverless vector store. Configure an Amazon Bedrock guardrail to block all types of PII during input and output processing. Configure Amazon Bedrock AgentCore to use the knowledge base and the guardrail to process and answer queries.

D.

Configure an Amazon Bedrock knowledge base that synchronizes with the S3 bucket by using semantic chunking. Configure the knowledge base to use an Amazon Aurora PostgreSQL vector store. Configure an Amazon Bedrock guardrail to block all types of PII during input and output processing. Configure Amazon Bedrock AgentCore to use the knowledge base and the guardrail to process and answer queries.

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Amazon Web Services AIP-C01 Summary

  • Vendor: Amazon Web Services
  • Product: AIP-C01
  • Update on: Oct 5, 2026
  • Questions: 161
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TESTED 05 Oct 2026