The correct answer is C because Continued Pre-training (also known as domain-adaptive pre-training) involves training a pre-trained model further on unlabeled domain-specific data. This method helps adapt the LLM to a specific domain without needing labeled datasets, making it ideal for cases where the goal is to enhance the model’s understanding of technical language or terminology.

From AWS documentation:

" Continued pre-training allows an LLM to ingest large volumes of domain-specific text without labels to improve contextual understanding in a particular area. This is effective when adapting a foundation model to new knowledge without altering the model architecture. "

Explanation of other options:

A. Full training refers to building a model from scratch, which is extremely resource-intensive and unnecessary if a strong base model already exists.

B. Supervised fine-tuning requires labeled data, which the scenario explicitly lacks.

D. RAG is a method to retrieve external information at inference time, not a training technique using unlabeled data.

Referenced AWS AI/ML Documents and Study Guides:

AWS Bedrock Model Customization Documentation – Continued Pre-training

Amazon SageMaker JumpStart – Domain Adaptation Techniques

AWS Machine Learning Specialty Study Guide – Foundation Model Customization Section