Free C1000-185 Practice Test Questions and Answers (2026) | Cert Empire

In the context of generative AI and large language models, text embeddings are a key component. What is the primary purpose of text embeddings in a retrieval-augmented generation (RAG) system, and how are they used?

When selecting parameters to optimize a prompt-tuned model experiment in IBM watsonx, which parameter is the most critical for controlling the model’s ability to generate coherent and contextually accurate responses?

You are reviewing the results of a prompt-tuning experiment where the goal was to improve an LLM's ability to summarize technical documentation. Upon inspecting the experiment results, you notice that the model has a high recall but relatively low precision. What does this likely indicate about the model’s performance, and how should you approach further tuning?

Which of the following practices are best suited to optimize the performance of a deployed generative AI model in IBM watsonx under real-world traffic conditions? (Select two)

In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?

You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents. Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?

In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?

You are working as a generative AI engineer and have developed a custom large language model (LLM) optimized for a specific use case. You are tasked with deploying this model on the IBM Watsonx platform. Which of the following steps is most essential to ensure the successful deployment of your custom model, given that the model uses a third-party transformer architecture?

When analyzing the results of a prompt tuning experiment, which two of the following actions are most appropriate if you observe a consistently high variance in model predictions across different prompt templates? (Select two)