AIP-C01 Question #23: Real Exam Question with Answer & Explanation

Question

A retail company is using Amazon Bedrock to develop a customer service AI assistant. Analysis shows that 70% of customer inquiries are simple product questions that a smaller model can effectively handle. However, 30% of inquiries are complex return policy questions that require advanced reasoning. The company wants to implement a cost-effective model selection framework to automatically route customer inquiries to appropriate models based on inquiry complexity. The framework must maintain high customer satisfaction and minimize response latency. Which solution will meet these requirements with the LEAST implementation effort?

Options

• ACreate a multi-stage architecture that uses a small foundation model (FM) to classify the
• BUse Amazon Bedrock intelligent prompt routing to automatically analyze inquiries. Route simple
• CImplement a single-model solution that uses an Amazon Bedrock mid-sized foundation model
• DCreate separate Amazon Bedrock endpoints for simple and complex inquiries. Implement a rule-

Explanation

Option B is the correct solution because it leverages native Amazon Bedrock intelligent prompt routing, which is specifically designed to reduce cost and complexity in multi-model GenAI architectures. Intelligent prompt routing automatically analyzes incoming prompts and selects the most appropriate foundation model based on prompt characteristics and complexity--without requiring custom classification logic or orchestration code. This approach directly meets the requirement for least implementation effort. The company does not need to deploy additional Lambda functions, maintain routing rules, or manage separate classification stages. Routing decisions are handled by Bedrock, which simplifies architecture and reduces operational risk. By routing the majority (70%) of simple product inquiries to smaller, lower-cost models, the company minimizes inference cost and latency. More complex return policy inquiries are automatically routed to larger models that provide better reasoning capabilities, preserving response quality and customer satisfaction. Because routing is handled inline by Bedrock, response latency remains low compared to multi- stage architectures that require an additional classification model call before inference. This is critical for customer service scenarios where responsiveness directly impacts satisfaction.

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