AWS Certified AI Practitioner AIF-C01 Practice Question
A start-up wants to expose its generative AI model through an API. Traffic is unpredictable and occasional delays of a few seconds are acceptable. To keep costs low while still using managed AWS infrastructure, which deployment option is the most appropriate?
Reserve Provisioned Throughput for the model on Amazon Bedrock.
Deploy the model with Amazon SageMaker Asynchronous Inference.
Create a SageMaker real-time endpoint with two always-on instances.
Run a SageMaker Batch Transform job for each incoming request.
SageMaker Asynchronous Inference does not keep instances provisioned. Instead, it automatically spins up resources when a request arrives and shuts them down when idle, charging only for the processing time and storage used. Because the service queues requests, it can absorb bursty traffic patterns, but this queueing means response latency may be several seconds, which is acceptable in the described scenario.
A real-time inference endpoint with fixed instances incurs hourly charges even when idle, raising costs.
Batch Transform is designed for offline, non-interactive jobs and is unsuitable for an API that must return individual responses.
Provisioned Throughput on Amazon Bedrock reserves dedicated capacity for consistently low latency, making it more expensive than the startup requires.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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