Free IBM C1000-185 Practice Test & Real Exam Questions

When optimizing a generative AI model using the Tuning Studio in IBM Watsonx, which two of the following actions can most effectively improve model performance when dealing with underfitting issues? (Select two)

You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?

When tuning the generative model parameters, which of the following scenarios describes an appropriate use of the maximum tokens setting, and how will it influence the model's output?

Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

You are configuring an LLM for a product recommendation chatbot. The goal is to balance creativity and relevance, ensuring the chatbot suggests diverse but appropriate products.
Which combination of model parameters will best achieve this? (Select two)

In a Retrieval-Augmented Generation (RAG) system, you are tasked with generating vector embeddings for a large corpus of documents. You plan to use a pre-trained transformer-based model to generate these embeddings.
What is the most important factor to consider when choosing a pre-trained model for generating embeddings in this scenario?

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?

You are fine-tuning a large language model (LLM) for a sentiment analysis task using customer reviews. The dataset is relatively small, so you decide to augment it using IBM InstructLab.
Which approach would be the most effective in generating high-quality synthetic data for this fine-tuning process?

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?