The correct choice is Foundation models are pre-trained on large, diverse datasets and can be fine-tuned or guided with prompts to handle many downstream tasks. These models learn broad patterns during large scale pre training and can be adapted through fine tuning or prompt engineering to produce personalized study guides and draft lesson materials.

Pre training on diverse data gives foundation models a general understanding of language and knowledge and this lets them be reused across tasks. Fine tuning or prompt guidance lets you steer outputs toward a subject domain or individual learner needs and retrieval augmented generation helps ground responses in course content and learner records.

Foundation models cannot personalize outputs based on learner interactions is false because models can accept context and user data and they can be combined with prompting and retrieval to produce tailored content.

Each foundation model is built for a single narrow use and cannot be adapted to other applications is incorrect since a key advantage of foundation models is generality and reuse across many tasks and domains.

On Amazon Bedrock, foundation models must be retrained from scratch for every subject domain is wrong because Amazon Bedrock provides managed access to multiple foundation models and supports customization methods that do not require full retraining from the ground up.

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