AIP-C01 · Question #38
A financial services company is developing a generative AI (GenAI) application that serves both premium customers and standard customers. The application uses AWS Lambda functions behind an Amazon…
The correct answer is C. Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Option C is the correct solution because AWS AppConfig is purpose-built to manage dynamic application configurations with low latency, strong validation, and minimal operational overhead, which directly matches the company's requirements. AWS AppConfig enables the company to…
Question
A financial services company is developing a generative AI (GenAI) application that serves both premium customers and standard customers. The application uses AWS Lambda functions behind an Amazon API Gateway REST API to process requests. The company needs to dynamically switch between AI models based on which customer tier each user belongs to. The company also wants to perform A/B testing for new features without redeploying code. The company needs to validate model parameters like temperature and maximum token limits before applying changes. Which solution will meet these requirements with the LEAST operational overhead?
Options
- ACreate AWS Systems Manager Parameter Store parameters for each configuration. Use Lambda
- BStore model configurations in Amazon DynamoDB tables. Optimize access patterns to retrieve
- CUse AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing.
- DCreate an Amazon ElastiCache (Redis OSS) cluster to store model configurations. Set short TTL
How the community answered
(20 responses)- A20% (4)
- B5% (1)
- C65% (13)
- D10% (2)
Explanation
Option C is the correct solution because AWS AppConfig is purpose-built to manage dynamic application configurations with low latency, strong validation, and minimal operational overhead, which directly matches the company's requirements. AWS AppConfig enables the company to centrally manage model selection logic, inference parameters, and customer-tier routing rules without redeploying Lambda functions. By using feature flags, the company can easily perform A/B testing of new models or prompt strategies by gradually rolling out changes to a subset of users or customer tiers. This allows experimentation and controlled releases without code changes. AppConfig also supports JSON schema validation, which is critical for validating parameters such as temperature, maximum token limits, and other model-specific settings before they are applied. This prevents invalid or unsafe configurations from being deployed and reduces the risk of runtime errors or degraded model behavior in production. Using the AWS AppConfig Agent allows Lambda functions to retrieve configurations efficiently with built-in caching and polling mechanisms, minimizing latency and avoiding excessive calls to configuration services. This approach scales well for high-throughput, low-latency applications such as GenAI APIs behind Amazon API Gateway.
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