AWS Certified AI Practitioner AIF-C01 Practice Question
A company is comparing several foundation models available in Amazon Bedrock and wants to minimize ongoing per-request spending. Which model attribute most directly affects the price charged for every 1,000 input or output tokens?
Whether the model was fine-tuned with reinforcement learning from human feedback (RLHF).
Whether the model supports multilingual prompts.
The model's parameter count and overall size.
The number of AWS Regions in which the model is deployed.
Amazon Bedrock pricing tiers are primarily based on the computational resources each model consumes per token processed. Larger models-with more parameters-require more GPUs and memory during inference, so AWS sets a higher per-token price for them. Attributes such as regional availability, multilingual capability, or the use of reinforcement learning from human feedback influence functionality but do not change the baseline token rate published for the model size.
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What are tokens in the context of foundation models?
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Why does the parameter count affect pricing in Amazon Bedrock?
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What is reinforcement learning from human feedback (RLHF)?
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Can you explain what tokens are in the context of foundation models?
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Why does a model's parameter count affect token pricing?
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What is Amazon Bedrock, and how does it simplify working with models?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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