AWS Certified Generative AI – Professional (AIP-C01) — Question 43

A company is developing a generative AI (GenAI)-powered customer support application that uses Amazon Bedrock foundation models (FMs). The application must maintain conversational context across multiple interactions with the same user. The application must run clarification workflows to handle ambiguous user queries. The company must store encrypted records of each user conversation to use for personalization. The application must be able to handle thousands of concurrent users while responding to each user quickly.
Which solution will meet these requirements?

Answer options

• A. Use an AWS Step Functions Express workflow to orchestrate conversation flow. Invoke AWS Lambda functions to run clarification logic. Store conversation history in Amazon RDS and use session IDs as the primary key.
• B. Use an AWS Step Functions Standard workflow to orchestrate clarification workflows. Include Wait for a Callback patterns to manage the workflows. Store conversation history in Amazon DynamoDPurchase on-demand capacity and configure server-side encryption.
• C. Deploy the application by using an Amazon API Gateway REST API to route user requests to an AWS Lambda function to update and retrieve conversation context. Store conversation history in Amazon S3 and configure server-side encryption. Save each interaction as a separate JSON file.
• D. Use AWS Lambda functions to call Amazon Bedrock inference APIs. Use Amazon SQS queues to orchestrate clarification steps. Store conversation history in an Amazon ElastiCache (Redis OSS) cluster. Configure encryption at rest.

Correct answer: B

Explanation

Option B is correct because it uses AWS Step Functions Standard workflows, which are suitable for long-running processes, and it supports the necessary orchestration of workflows and encapsulation of callback patterns. The use of Amazon DynamoDB for storing conversation history provides scalability and secure storage options. The other options either do not meet the performance requirements, use less suitable storage solutions, or do not effectively manage the workflows needed for clarification.