computation time for each request. B. Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability. C. Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task -specific prompts. D. Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on- the-fly customization of responses. E. Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time -consuming. You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero -shot or a few -shot prompting technique with an IBM Watsonx model. Which of the following statements best describes the key difference between zero -shot and few -shot prompting?
generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions. Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?
B. A graph database like Neo4j, which is designed for traversing relationships between data points. C. A vector database like Pinecone or Weaviate that supports approximate nearest neighbor (ANN) search. D. Relational databases with B -tree indexes. You are working on a project that involves deploying a series of prompt templates for a large language model on the IBM Watsonx platform. The team has requested a system that supports prompt versioning so that updates to the prompts can be tracked and tested over time. Which of the following is the most important consideration when planning prompt versioning for deployment?
stage, ensuring control over the process at every step. C. Use watsonx.ai to generate a summary immediately, and then perform NLP analysis and document retrieval in parallel to verify the accuracy of the output. D. Perform document retrieval first, followed by NLP analysis to extract relevant information, and then pass the processed data to watsonx.ai for summarization. You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic -looking data without focusing on distribution conformity. Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson -Darling test?
B. It improves the model’s generalization by exposing it to a wider variety of data points and scenarios. C. It creates a larger training dataset by duplicating and randomizing the existing data, which enhances model accuracy. D. It eliminates the need for any human intervention in the fine -tuning process. Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?
➢ 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?
