commands and reasons for failure." You are working on generating synthetic training data using IBM InstructLab to supplement a small dataset for a question -answering system. Which strategy would most effectively enhance the dataset without introducing biases or artifacts?
You are developing a Retrieval -Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently. Which of the following best describes the appropriate approach to text chunking for this RAG implementation?
reduce performance degradation. C. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process. D. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information. IBM Watsonx Tuning Studio allows users to fine -tune pre -trained models for their specific use cases. Which of the following correctly describes the primary benefits of using Tuning Studio for optimizing a generative AI model?
You are generating a list of items using IBM watsonx’s generative AI, but you notice that the model sometimes cuts off mid-sentence when using a stop sequence. What could be the best approach to ensure that the model finishes generating complete sentences while also stopping after a specific sequence is reached?
contextually appropriate responses. Which of the following steps are essential when preparing the dataset for prompt -tuning in this context? (Select two)
➢ TOTAL QUESTIONS: 379 In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few -shot prompting techniques. Which of the following best explains the key difference between zero -shot and few -shot prompting?
