AIP-C01 · Question #40
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…
The correct answer is B. Use an AWS Step Functions Standard workflow to orchestrate clarification workflows. Include. Option B is the correct solution because it provides a scalable, durable, and secure architecture for conversational GenAI workloads that require multi-step clarification workflows and persistent AWS Step Functions Standard workflows are designed for long-running, stateful…
Question
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?
Options
- AUse an AWS Step Functions Express workflow to orchestrate conversation flow. Invoke AWS
- BUse an AWS Step Functions Standard workflow to orchestrate clarification workflows. Include
- CDeploy the application by using an Amazon API Gateway REST API to route user requests to an
- DUse AWS Lambda functions to call Amazon Bedrock inference APIs. Use Amazon SQS queues
How the community answered
(40 responses)- A8% (3)
- B68% (27)
- C5% (2)
- D20% (8)
Explanation
Option B is the correct solution because it provides a scalable, durable, and secure architecture for conversational GenAI workloads that require multi-step clarification workflows and persistent AWS Step Functions Standard workflows are designed for long-running, stateful workflows with high reliability, which is ideal for clarification loops that may require multiple back-and-forth interactions. The Wait for a Callback pattern allows the workflow to pause while awaiting additional user input, making it well-suited for handling ambiguous queries without losing execution state. Storing conversation history in Amazon DynamoDB enables millisecond-latency reads and writes at massive scale, supporting thousands of concurrent users. DynamoDB's on-demand capacity mode automatically scales with traffic, eliminating capacity planning. Server-side encryption ensures that stored conversation data is encrypted at rest, meeting security and compliance requirements for personalized data.
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