RELIABLE C1000-185 EXAM PRACTICE, C1000-185 VALID TORRENT

Reliable C1000-185 Exam Practice, C1000-185 Valid Torrent

Reliable C1000-185 Exam Practice, C1000-185 Valid Torrent

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Tags: Reliable C1000-185 Exam Practice, C1000-185 Valid Torrent, New C1000-185 Braindumps Questions, Exam C1000-185 Question, C1000-185 Latest Training

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q372-Q377):

NEW QUESTION # 372
You are using the IBM watsonx platform to fine-tune an existing generative AI model for a text-based customer support system. Due to privacy constraints, the company cannot use real customer data, so synthetic data must be generated using the platform's UI. Your task is to create and configure the synthetic data and fine-tune the model to optimize customer query handling.
Which of the following actions will best ensure the synthetic data generation process leads to a well-tuned model that handles diverse customer queries effectively? (Select two)

  • A. Simulate both simple and complex customer queries, including ambiguous or vague requests, in the synthetic data.
  • B. Prioritize the generation of synthetically perfect customer queries with no grammatical errors to ensure high-quality data.
  • C. Fine-tune the model immediately after generating synthetic data, without further inspection, to maintain efficiency.
  • D. Leverage the user interface to create synthetic data that includes rare edge cases, such as technical support questions or multi-part inquiries.
  • E. Generate a synthetic dataset that only covers the most frequent customer queries to reduce complexity in model training.

Answer: A,D


NEW QUESTION # 373
You are developing a Retrieval-Augmented Generation (RAG) system for a question-answering application. The system relies on generating vector embeddings to retrieve relevant documents based on the input query.
What is the key advantage of using vector embeddings for document retrieval in a RAG pipeline compared to traditional keyword-based search methods?

  • A. Vector embeddings represent text as fixed-length vectors, allowing for faster indexing but no improvements in retrieval accuracy.
  • B. Vector embeddings capture the semantic meaning of text, allowing for more accurate retrieval of contextually similar documents, even if they do not share exact keywords with the query.
  • C. Vector embeddings increase the memory requirements of the system, making retrieval slower but improving the generation quality of the model.
  • D. Vector embeddings do not provide any meaningful improvement over keyword-based methods unless combined with reinforcement learning algorithms.

Answer: B


NEW QUESTION # 374
After completing a prompt-tuning experiment, you notice that the model's accuracy in generating relevant responses is high, but the fluency and grammatical correctness of the outputs seem to be suboptimal.
What statistical metric would most directly indicate this issue, and what action should you take to improve the output?

  • A. BLEU score; improve prompt engineering to ensure that the model focuses on fluency.
  • B. F1 score; increase the training dataset size to improve overall accuracy.
  • C. Perplexity score; apply additional language model fine-tuning on grammatical correctness.
  • D. ROUGE score; adjust the token generation limit to ensure longer outputs.

Answer: C


NEW QUESTION # 375
You are tasked with optimizing a generative AI model's usage in a chatbot that provides troubleshooting instructions for software issues. The current prompt template is:
"Please provide step-by-step troubleshooting instructions for the following issue: [Issue Description]. Be detailed, include specific commands or settings the user should check, and provide potential reasons for failure." To reduce the token count and ensure cost efficiency, which of the following prompt template modifications would best manage token usage while preserving essential information?

  • A. "Provide step-by-step instructions for troubleshooting the issue: [Issue Description]. Include commands and reasons for failure."
  • B. "Give troubleshooting instructions for [Issue Description], including steps, commands, and reasons for failure."
  • C. "Provide detailed troubleshooting instructions for the issue: [Issue Description], with steps, commands, and potential reasons for failure."
  • D. "Troubleshoot the following issue: [Issue Description]. Offer step-by-step commands and reasons for failure."

Answer: A


NEW QUESTION # 376
IBM Watsonx Tuning Studio offers several benefits when fine-tuning pre-trained models for specific tasks.
Which of the following is not a key benefit of using Tuning Studio?

  • A. Tuning Studio integrates with existing AI infrastructure to streamline model fine-tuning without requiring complex deployment processes.
  • B. Tuning Studio offers fine-tuning with minimal data, allowing users to adapt models to niche tasks without needing large datasets.
  • C. Tuning Studio enables real-time, dynamic adjustments to the model's architecture during inference to handle new tasks.
  • D. Tuning Studio allows selective parameter updates, reducing the need to retrain the entire model for each new task.

Answer: C


NEW QUESTION # 377
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