AIP-C01 · Question #68
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive…
The correct answer is A. Develop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to. Option A is the correct solution because it provides proactive, model-aware token management with fine-grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not…
Question
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution. The token management solution must proactively alert when applications approach model- specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units. Which solution will meet these requirements?
Options
- ADevelop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to
- BImplement Amazon Bedrock Guardrails with token quota policies. Capture metrics on rejected
- CDeploy an Amazon SQS dead-letter queue for failed requests. Configure an AWS Lambda
- DUse Amazon API Gateway to create a proxy for all Amazon Bedrock API calls. Configure request
How the community answered
(53 responses)- A85% (45)
- B2% (1)
- C4% (2)
- D9% (5)
Explanation
Option A is the correct solution because it provides proactive, model-aware token management with fine-grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not natively enforce proactive, model-specific token limits across multiple applications or business units. By implementing model-specific tokenizers in AWS Lambda, the company can estimate input and output token usage before sending requests to Amazon Bedrock. This enables early detection of requests that are approaching or exceeding model limits and allows the application to block, truncate, or reroute requests proactively rather than reacting to failures. Publishing token usage metrics to Amazon CloudWatch enables real-time monitoring and alerting at scale, easily supporting more than 5,000 requests per minute. Storing detailed token usage data in Amazon DynamoDB allows the company to attribute usage and costs to specific applications, teams, or business units--an essential requirement for regulatory reporting and internal chargeback.
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