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A media company is building an AI-powered content moderation system by using Amazon Bedrock.

A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model.

The system must respond in near real time for high-confidence results. The system must process low-confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system.

Which solution will meet these requirements?

A.

Use Amazon API Gateway to invoke the small model synchronously. If the small model’s confidence score is below 0.65, synchronously call the larger model. Use provisioned concurrency to handle traffic spikes.

B.

Use an AWS Step Functions workflow that has parallel branches to run both the small model and the large model for every request. Choose the large model result when confidence score values differ.

C.

Send requests to an Amazon SQS queue. Use AWS Fargate to process messages. Invoke the small model first. If the confidence score is below 0.65, place the request in a second SQS queue to process asynchronously by using the large model.

D.

Deploy both models on Amazon EC2 instances and enable auto scaling. Use a custom application heuristic to route requests to the appropriate instance based on phrase length and keyword rules.

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