Provide labeled data to customize a model to improve performance on specific tasks.
Correct Answer: Fine-tuning
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI guidance defines fine-tuning as the process of adapting a pre-trained foundation model using labeled, task-specific data . Fine-tuning adjusts the model’s parameters so it performs better on a particular task, such as classification, summarization, or domain-specific reasoning.
Fine-tuning is commonly used when:
High-quality labeled data is available
The goal is to improve accuracy on a specific task
The base FM already has strong general capabilities
AWS AI Study Guide References:
AWS foundation model customization methods
AWS fine-tuning concepts for generative AI
Provide unlabeled data to customize a foundation model for a specific domain.
Correct Answer: Continued pre-training
Comprehensive and Detailed Explanation (AWS AI documents):
AWS documentation describes continued pre-training as extending the training of a foundation model using large volumes of unlabeled, domain-specific data . This method helps the model better understand domain vocabulary, structure, and context without requiring labeled datasets.
Continued pre-training is useful when:
Large amounts of unlabeled domain data are available
The goal is to improve domain understanding rather than a single task
Labeling data would be expensive or impractical
AWS AI Study Guide References:
AWS generative AI training lifecycle
AWS guidance on domain adaptation using unlabeled data
Transfer knowledge from a larger and more intelligent model to a smaller model.
Correct Answer: Distillation
Comprehensive and Detailed Explanation (AWS AI documents):
AWS generative AI materials define distillation as a technique where a smaller model (student) learns to replicate the behavior of a larger, more capable model (teacher) . The goal is to retain most of the performance while reducing model size, cost, and inference latency.
Distillation is commonly used to:
AWS AI Study Guide References:
AWS model optimization techniques
AWS knowledge distillation concepts
