B. Lowering the temperature to 0.1 C. Increasing the repetition penalty to 1.2 D. Reducing the beam size to 1 IBM Watsonx Tuning Studio provides multiple benefits for optimizing pre -trained models for specific tasks. Which of the following correctly describes one of the primary benefits of using Tuning Studio for model optimization?
In a scenario where a large language model (LLM) is integrated into a customer support application, the model is designed to retrieve relevant product information to answer complex user queries. The dataset consists of diverse product documents, including PDFs, user manuals, and website pages. Which of the following best describes when to use a vector database as part of the Retrieval -Augmented Generation (RAG) approach?
this context?
user feedback over multiple generations. B. Decoding is the process where the model generates a response token -by-token, choosing each token based on the probability distribution over all possible tokens. C. Decoding involves translating the input data into a format that the AI model can understand before generating an output. D. Decoding is the final step in training, where the AI model verifies the accuracy of its outputs against a predefined set of labels. In the context of Retrieval -Augmented Generation (RAG) models, embeddings play a crucial role in retrieving relevant documents. Which of the following best describes the purpose of embeddings in GenAI, particularly within a RAG system?
the validation set. What could be done to address the overfitting issue and improve the model's generalization? (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?
