AWS Certified AI Practitioner AIF-C01 Practice Question
An organization wants to run a large language model and is considering two options: deploy the model as a custom container on Amazon SageMaker or call an equivalent foundation model through Amazon Bedrock. Which cost implication is specific to the SageMaker option?
The team is billed for the GPU inference instances while they remain provisioned, even if no requests are served.
Model artifact storage in Amazon S3 is included at no additional cost.
Charges apply only for the number of input and output tokens processed.
Cross-Region data transfer fees are automatically waived.
With a custom model hosted on Amazon SageMaker, the company must provision one or more GPU-based inference instances. SageMaker charges for these instances on a per-second (instance-hour) basis for as long as they are running, even when the endpoint is idle. By contrast, Amazon Bedrock consumes no always-on capacity; customers are billed primarily per input and output token, so costs accrue only when the model is actually invoked. Storage fees, token-based pricing, and waived data-transfer charges are not unique to SageMaker hosting.
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What are GPU inference instances in Amazon SageMaker?
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How does Amazon Bedrock’s cost structure differ from Amazon SageMaker’s?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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