AWS Certified Generative AI – Professional (AIP-C01) — Question 63

A company is building a serverless application that uses AWS Lambda functions to help students around the world summarize notes. The application uses Anthropic Claude through Amazon Bedrock. The company observed that most of the traffic occurs during evenings in each time zone. Users report experiencing throttling errors during peak usage times in their times zones.
The company needs to resolve the throttling issues by ensuring continuous operation of the application. The solution must maintain application performance quality. The company needs a solution that does not require a fixed hourly cost during low traffic periods.
Which solution will meet these requirements?

Answer options

• A. Create custom Amazon CloudWatch metrics to monitor model errors. Set provisioned throughput to a value that is safely higher than the peak traffic observed.
• B. Create custom Amazon CloudWatch metrics to monitor model errors. Set up a failover mechanism to redirect invocations to a backup AWS Region when the errors exceed a specified threshold.
• C. Enable invocation logging in Amazon Bedrock. Monitor key metrics such as Invocations, InputTokenCount, OutputTokenCount, and Invocation Throttles. Distribute traffic across cross-Region inference endpoints.
• D. Enable invocation logging in Amazon Bedrock. Monitor InvocationLatency, InvocationClientErrors, and InvocationServerErrors metrics. Distribute traffic across multiple versions of the same model.

Correct answer: C

Explanation

Option C is correct because it addresses the need to monitor key metrics and distribute traffic effectively, which can alleviate throttling issues during peak times. Option A focuses on provisioned throughput, which may incur costs regardless of usage. Option B, while considering failover, does not directly address traffic management during peak usage. Option D looks at version distribution but doesn't specifically target cross-Region traffic management, which is crucial for maintaining performance.