AWS Certified AI Practitioner Practice Exam

How can a foundation model be improved to handle complex scientific terms in a dataset?

1. Use a few-shot prompting to define how the FM can answer the questions

2. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms

3. Change the FM inference parameters

4. Clean the research paper data to remove complex scientific terms

The correct answer is: Use domain adaptation fine-tuning to adapt the FM to complex scientific terms

Using domain adaptation fine-tuning to adapt the foundation model (FM) to complex scientific terms is a highly effective approach because it specifically targets the enhancement of the model's performance within a particular domain or context. In this case, the domain consists of scientific texts that may include unique terminology, concepts, and language structures that differ from the general language the model was originally trained on. Domain adaptation fine-tuning involves leveraging additional labeled datasets that contain examples of the complex scientific terms the model will need to understand and utilize effectively. By fine-tuning the model on this specialized data, it learns the specific relationships, nuances, and meanings of these terms, which can improve its accuracy and relevance in generating responses or outcomes related to scientific queries. This method ensures that the foundation model becomes more attuned to the specific language and knowledge of the scientific domain. In contrast, the other choices may not address the underlying issue effectively. Few-shot prompting may provide guidance for specific instances but does not fundamentally alter the model's understanding of complex terms. Changing inference parameters might adjust performance in a broad sense, but without addressing the model's training on scientific concepts, it may not improve handling of specific terminology. Cleaning the research paper data to remove complex terms would dilute the dataset of valuable and