AWS Certified AI Practitioner AIF-C01 Practice Question
A startup wants to customize a foundation model without running any additional training jobs. Instead, it will include several example question-answer pairs inside each prompt (in-context learning). What is the primary cost implication of using this method?
Higher per-inference charges because the longer prompt increases the number of billed tokens.
Additional licensing fees required for changing the model's underlying weights.
A one-time GPU training cost comparable to full model fine-tuning.
Ongoing storage fees for maintaining a separate, customized model checkpoint in Amazon S3.
In-context learning relies on placing examples directly in the prompt, so there is no training or model-hosting expense. The tradeoff is that every extra token in the prompt is billed at inference time, making each request more expensive when prompts become lengthy. The other options describe costs that apply to fine-tuning (training compute), model hosting, or license fees for weight modifications, none of which occur when only adjusting the prompt.
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What are tokens in the context of AI language models?
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How does in-context learning differ from fine-tuning a model?
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Why do longer prompts increase inference costs in foundation models?
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What is in-context learning in AI?
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What does 'billed tokens' mean in relation to AI model inference?
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How does in-context learning differ from fine-tuning a model?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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