RELIABLE C1000-185 TEST PRICE | C1000-185 NEW STUDY GUIDE

Reliable C1000-185 Test Price | C1000-185 New Study Guide

Reliable C1000-185 Test Price | C1000-185 New Study Guide

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Tags: Reliable C1000-185 Test Price, C1000-185 New Study Guide, C1000-185 Exam Questions Answers, C1000-185 Free Learning Cram, New C1000-185 Learning Materials

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
In the context of a Retrieval-Augmented Generation (RAG) system, which type of retriever is best suited for retrieving documents based on semantic similarity in a vector space?

  • A. Dense retriever, which converts both the query and documents into vectors and retrieves based on similarity in the vector space.
  • B. Boolean retriever, which uses logical operators (AND, OR, NOT) to filter documents based on keyword presence.
  • C. Hierarchical retriever, which first applies keyword-based filters and then ranks documents using machine learning models.
  • D. Exact match retriever, which retrieves documents that exactly match the query string.

Answer: A


NEW QUESTION # 18
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. Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
  • B. Store all prompt versions directly in the model's code repository, updating the main branch with each new version.
  • C. Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.
  • D. Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.

Answer: C


NEW QUESTION # 19
When optimizing a prompt-tuned model, which parameter adjustment would most likely help prevent overfitting without negatively impacting the model's ability to generalize to unseen prompts?

  • A. Reducing the model's learning rate
  • B. Removing weight decay entirely
  • C. Increasing the model's dropout rate
  • D. Increasing the model's learning rate

Answer: C


NEW QUESTION # 20
Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?

  • A. Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
  • B. Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
  • C. Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.
  • D. Prompt variables ensure that the same text input will always yield the same output, improving consistency.

Answer: B


NEW QUESTION # 21
You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai's large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents.
What is the best approach to achieve this integration?

  • A. Use Watson Discovery for summarizing documents and watsonx.ai's LLM for only retrieving relevant content from the documents.
  • B. Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai's LLM for summarization through API calls.
  • C. Use watsonx.ai's LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.
  • D. Use watsonx.ai's LLM to both retrieve and summarize the documents, bypassing Watson Discovery.

Answer: B


NEW QUESTION # 22
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