AWS Certified AI Practitioner AIF-C01 Practice Question
A fintech startup needs to adjust an off-the-shelf large language model so it understands its proprietary financial terminology. The team has only a small labeled dataset and a limited budget. Which model selection criterion best supports this requirement?
The model is pre-trained on more than 200 languages.
The model offers built-in support for adapter-based or LoRA fine-tuning.
The model can generate tokens with sub-50 ms latency.
The model includes integrated vision-language encoders for image captioning.
When an organization must adapt a foundation model with a small, task-specific dataset and wants to minimize cost and training time, it should choose a model that explicitly supports parameter-efficient customization techniques such as adapters or LoRA-based fine-tuning. These methods let customers add or update only a small fraction of the model's weights, avoiding the expense of full retraining. Multilingual coverage, low latency, and multimodal capabilities are valuable for other use cases but do not directly address the need for affordable, data-efficient customization.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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