B. Re-training the model from scratch on your dataset. C. Utilizing data augmentation techniques to create synthetic data for the model. D. Using transfer learning to adapt the model to your dataset by updating only the final layers. Explanation: D. Transfer learning involves adapting a pre-trained model to new tasks by updating specific parts of the model, such as the final layers, using your specific dataset. This approach is more efficient than retraining from scratch. Why Other Options are Incorrect:
Language Models (LLMs)?
Which is a characteristic of LoRA (Low -Rank Adaptation) fine-tuning for Large Language Models (LLMs)?
B. It does not support integration with other OCI services. C. It requires extensive manual configuration for model training. D. It provides pre -trained models for text and image generation. Explanation: OCI Generative AI Service offers pre-trained models for generating both text and images, facilitating various AI applications without requiring users to build models from scratch. Why Other Options Are Incorrect:
C. Incorporating code snippets without any comments or documentation. D. Limiting the training data to syntactically perfect code snippets only. Explanation: Training the model on a diverse dataset that includes multiple programming languages helps improve the model’s accuracy and versatility in providing code suggestions. Why Other Options Are Incorrect:
LangSmith Monitoring is designed to track usage patterns of language models, providing insights into how models are being used and performing in production. Why Other Options Are Incorrect:
