PRACTICE C1000-185 TEST - VALID C1000-185 DUMPS DEMO

Practice C1000-185 Test - Valid C1000-185 Dumps Demo

Practice C1000-185 Test - Valid C1000-185 Dumps Demo

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q154-Q159):

NEW QUESTION # 154
A generative AI model is given the following prompt: "Translate the following sentence into French: 'The sun is shining brightly today.'" No additional context or examples are provided.
This is an example of which type of prompting and why is it likely to succeed?

  • A. Zero-shot prompting, because the model is expected to perform the task without any example.
  • B. Few-shot prompting, because the task requires translation examples to guide the model.
  • C. Zero-shot prompting, but it will only succeed if the prompt includes a few additional translation examples.
  • D. Zero-shot prompting, but it will likely fail since translation tasks always need examples for accuracy.

Answer: A


NEW QUESTION # 155
You are working with IBM Watsonx to develop a generative AI solution that automatically generates product descriptions for an e-commerce website. The descriptions need to be concise, factual, and include important product features like size, color, and material.
Which prompt design approach would best ensure the output meets these requirements?

  • A. "Generate a product description that highlights the unique aspects of the product and uses emotional language to engage the reader."
  • B. "Write a summary that provides information on each product, making the content engaging, humorous, and memorable."
  • C. "Generate a creative and imaginative product description for the items listed below."
  • D. "Provide a product description for the following items, ensuring it is factual, concise, and includes specific details such as size, color, and material."

Answer: D


NEW QUESTION # 156
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?

  • A. Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.
  • B. Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.
  • C. Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.
  • D. Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.

Answer: A


NEW QUESTION # 157
In a Retrieval-Augmented Generation (RAG) system, embeddings play a central role in linking input queries with relevant external knowledge. Different embedding models can be used to generate these embeddings.
Which of the following embedding models is best suited for capturing semantic meaning in text for use in a RAG system?

  • A. Word2Vec
  • B. Latent Dirichlet Allocation (LDA)
  • C. Bag-of-Words (BoW)
  • D. One-Hot Encoding

Answer: A


NEW QUESTION # 158
You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

  • A. Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.
  • B. Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.
  • C. Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
  • D. Store all prompt versions directly in the model's code repository, updating the main branch with each new version.

Answer: A


NEW QUESTION # 159
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