AWS Certified AI Practitioner AIF-C01 Practice Question
A company plans to call an LLM through Amazon Bedrock at a steady 150 requests per second around the clock. The team needs consistent low latency and wants to lock in a predictable monthly price instead of paying variable on-demand invocations. Which Bedrock pricing option should they choose?
Run the model with Amazon SageMaker serverless inference so it scales automatically.
Continue using on-demand, pay-per-request invocations.
Purchase provisioned throughput for the model and reserve the required model units.
Move the workload to PartyRock for free experimentation.
Amazon Bedrock offers two main invocation models. On-demand pricing charges per input and output token and can fluctuate with usage, so cost is less predictable. Provisioned throughput lets customers reserve "model units" for a fixed hourly rate. Reserving those units guarantees the needed concurrency, delivers consistent low-latency responses, and makes the bill predictable-ideal for a steady, 24×7 workload. SageMaker serverless inference and PartyRock are different services; they do not provide Bedrock pricing controls. Therefore, reserving provisioned throughput is the correct choice.
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How does Amazon Bedrock's on-demand pricing differ from provisioned throughput?
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Can Amazon SageMaker serverless inference replace Bedrock for LLM workloads?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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