AWS Certified Generative AI – Professional (AIP-C01) — Question 48

A company has a generative AI (GenAI) application that uses Amazon Bedrock to provide real-time responses to customer queries. The company has noticed intermittent failures with API calls to foundation models (FMs) during peak traffic periods.
The company needs a solution to handle transient errors and provide detailed observability into FM performance. The solution must prevent cascading failures during throttling events and provide distributed tracing across service boundaries to identify latency contributors. The solution must also enable correlation of performance issues with specific FM characteristics.
Which solution will meet these requirements?

Answer options

• A. Implement a custom retry mechanism with a fixed delay of 1 second between retries. Configure Amazon CloudWatch alarms to monitor the application's error rates and latency metrics.
• B. Configure the AWS SDK with standard retry mode and exponential backoff with jitter. Use AWS X-Ray tracing with annotations to identify and filter service components.
• C. Implement client-side caching of all FM responses. Add custom logging statements in the application code to record API call durations.
• D. Configure the AWS SDK with adaptive retry mode. Use AWS CloudTrail distributed tracing to monitor throttling events.

Correct answer: B

Explanation

Option B is correct because it integrates a standard retry strategy with exponential backoff, which helps manage transient errors effectively, and AWS X-Ray provides the necessary distributed tracing to identify latency issues across services. Option A lacks the sophisticated retry mechanism and distributed tracing capabilities, while C's client-side caching does not address the need for observability and error handling. Option D incorrectly suggests AWS CloudTrail, which is not designed for distributed tracing of application performance.