FREE PDF QUIZ ACCURATE IBM - C1000-185 - IBM WATSONX GENERATIVE AI ENGINEER - ASSOCIATE EXAMS TORRENT

Free PDF Quiz Accurate IBM - C1000-185 - IBM watsonx Generative AI Engineer - Associate Exams Torrent

Free PDF Quiz Accurate IBM - C1000-185 - IBM watsonx Generative AI Engineer - Associate Exams Torrent

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q204-Q209):

NEW QUESTION # 204
You are tasked with fine-tuning a pre-trained large language model (LLM) on a custom dataset containing customer support interactions for a company. The dataset contains text with specific categories related to issues such as billing, product returns, technical support, and feature requests. Before training, you need to prepare the dataset for optimal fine-tuning.
Which of the following steps is the most crucial to ensure the dataset is prepared effectively for fine-tuning the model?

  • A. Perform a spelling correction on the entire dataset to remove any language inconsistencies.
  • B. Manually categorize each interaction and organize them into a taxonomy tree structure.
  • C. Tokenize the dataset before curating it and mapping it to the taxonomy tree.
  • D. Convert all text to lowercase to ensure uniformity in the dataset.

Answer: B


NEW QUESTION # 205
In the context of generative AI optimization, you are tasked with improving the model's response accuracy across different domains. One suggestion is to use soft prompts for enhanced performance.
How does a soft prompt differ from a hard prompt in terms of implementation and flexibility?

  • A. Soft prompts can dynamically change during inference based on the input data, whereas hard prompts are fixed throughout the entire generation process.
  • B. Soft prompts use a rule-based system to guide the model, while hard prompts depend on statistical techniques to generate responses.
  • C. Hard prompts are stored as vector embeddings within the model, while soft prompts are manually crafted by users to optimize generation.
  • D. Soft prompts are embedded during the model's training and rely on learned representations, while hard prompts are explicit text-based inputs given during inference.

Answer: D


NEW QUESTION # 206
You are fine-tuning a large language model (LLM) with InstructLab to generate high-quality summaries for long-form text documents.
After conducting an initial experiment, the performance seems suboptimal, particularly on technical documents with specialized vocabulary.
What steps could you take to improve the fine-tuning process to achieve better performance? (Select two)

  • A. Perform prompt engineering by providing specific task instructions for summarization.
  • B. Increase the learning rate for better adaptation to specialized data.
  • C. Experiment with a smaller model to reduce complexity and increase inference speed.
  • D. Add more domain-specific training data to improve the model's understanding of technical vocabulary.
  • E. Use greedy decoding during inference to generate concise summaries.

Answer: A,D


NEW QUESTION # 207
You are designing a document search application using IBM Watson's RAG architecture. You need to generate vector embeddings for your document corpus, which consists of unstructured text data. The vector embeddings will be used for similarity search in conjunction with a retriever model.
Which of the following steps is a prerequisite to generating effective vector embeddings from the unstructured text using an embedding API?

  • A. Preprocessing the text by tokenizing it into individual words and removing stop words
  • B. Choosing a transformer-based model pre-trained on a domain-specific corpus to generate the vector embeddings
  • C. Ensuring that the text data is fully normalized, including removing punctuation and converting all text to lowercase
  • D. Compressing the text data to reduce its dimensionality before passing it to the embedding API

Answer: B


NEW QUESTION # 208
You are tasked with optimizing a prompt-tuned large language model (LLM) using IBM Watsonx for a customer service chatbot. The chatbot needs to handle a variety of tasks, such as answering frequently asked questions (FAQs), providing detailed product descriptions, and troubleshooting user issues.
What is the most appropriate task to focus on during the initial tuning experiment?

  • A. Fine-tune the model to generate product descriptions using longer contextual prompts.
  • B. Focus on prompt-tuning the model for multi-turn dialogue to simulate more natural conversations.
  • C. Optimize the model for extractive question-answering from a predefined knowledge base.
  • D. Tune the model for text summarization, condensing user queries into shorter forms.

Answer: C


NEW QUESTION # 209
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