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C1000-185 valid test torrent & C1000-185 reliable test vce & C1000-185 t

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C1000-185 valid test torrent & C1000-185 reliable test vce & C1000-185 t

Posted at 14 hour before      View：1 | Replies：0        Print      Only Author   [Copy Link] 1#
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q140-Q145):NEW QUESTION # 140
You are using IBM's Tuning Studio to fine-tune a large-scale foundation model for a customer service chatbot. The goal is to optimize the model for performance in handling a wide variety of customer queries while minimizing computational costs. Before making any changes, you want to understand how Tuning Studio can help achieve your optimization goals.
Which of the following is the most significant benefit provided by Tuning Studio when optimizing a generative AI model?
  • A. Tuning Studio provides real-time monitoring of model performance metrics during the fine-tuning process, allowing you to adjust hyperparameters effectively.
  • B. Tuning Studio reduces the dataset size needed for training by implementing automated data augmentation strategies.
  • C. Tuning Studio allows the user to implement custom model architectures from scratch to meet specific task requirements.
  • D. Tuning Studio automatically deploys the fine-tuned model to production environments without requiring further testing.
Answer: A

NEW QUESTION # 141
Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?
  • A. Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
  • B. Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.
  • C. Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
  • D. Prompt variables ensure that the same text input will always yield the same output, improving consistency.
Answer: A

NEW QUESTION # 142
While preparing a fine-tuning project using IBM watsonx, you want to generate synthetic data via the User Interface (UI) to supplement your existing dataset.
Which of the following describes an option supported by IBM watsonx for synthetic data generation through the UI?
  • A. Using pre-built templates to generate domain-specific synthetic data
  • B. Manually coding data augmentation scripts within the UI
  • C. Uploading real-time streaming data for immediate synthetic generation
  • D. Directly modifying the architecture of the model to generate task-specific synthetic data
Answer: A

NEW QUESTION # 143
You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits.
Which configuration would help minimize generation costs while ensuring an accurate summary of the document?
  • A. Set the maximum token limit to 300 and the minimum token limit to 0, allowing the model to generate a brief summary of the most relevant points while avoiding excessive verbosity.
  • B. Set the maximum token limit to 500 and the minimum token limit to 250 to ensure a balanced summary of key sections with some detailed
  • C. Set both the minimum and maximum token limits to 1,000 to ensure the entire report is captured in detail, with no important information left out.
  • D. Set the maximum token limit to 700 and the minimum token limit to 600 to ensure a well-rounded summary with all necessary sections and detailed information included.
Answer: A

NEW QUESTION # 144
You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?
  • A. Focus only on neutral and negative examples to challenge the model
  • B. Increase the number of positive sentiment examples in the dataset
  • C. Ensure an equal distribution of positive, negative, and neutral sentiment examples
  • D. Convert neutral examples into either positive or negative to simplify the task
Answer: C

NEW QUESTION # 145
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