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[General] Free C1000-185 Sample|Sound for IBM watsonx Generative AI Engineer - Associate

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【General】 Free C1000-185 Sample|Sound for IBM watsonx Generative AI Engineer - Associate

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q243-Q248):NEW QUESTION # 243
When setting up a tuning experiment in IBM watsonx's Tuning Studio, which of the following best describes the process for optimizing a model's hyperparameters?
  • A. Set the learning rate to its maximum value to speed up the tuning process and reduce experimentation time.
  • B. All hyperparameters should be fixed at the default settings to ensure consistency across different experiments.
  • C. Use automated hyperparameter search techniques such as grid search or random search to explore multiple configurations efficiently.
  • D. Manually adjust one hyperparameter at a time while keeping all other parameters constant to precisely identify its impact on performance.
Answer: C

NEW QUESTION # 244
You are tasked with fine-tuning a large language model (LLM) using IBM's InstructLab to improve performance for a specific customer service task. The goal is to enhance the model's ability to answer questions related to account management and customer complaints.
Which of the following actions is NOT a component of the fine-tuning process in InstructLab?
  • A. Defining specific task instructions that the model will follow during inference
  • B. Directly adjusting the model's architecture to increase the number of attention heads in the transformer
  • C. Selecting and preprocessing a representative dataset of customer interactions for training
  • D. Tuning the learning rate to prevent overfitting during the fine-tuning process
Answer: B

NEW QUESTION # 245
When optimizing a generative AI model using the Tuning Studio in IBM Watsonx, which two of the following actions can most effectively improve model performance when dealing with underfitting issues? (Select two)
  • A. Reduce the learning rate
  • B. Increase the model's complexity by adding more layers
  • C. Enable early stopping
  • D. Increase the number of training epochs
  • E. Decrease the batch size
Answer: B,D

NEW QUESTION # 246
You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?
  • A. Lower the temperature to reduce randomness in the model's response.
  • B. Incorporate additional training data from underrepresented demographic groups.
  • C. Use a prompt that explicitly asks for neutrality across demographic groups.
  • D. Switch to using deterministic (greedy) decoding to ensure more consistent outputs
Answer: B

NEW QUESTION # 247
In the context of prompt engineering, how does the repetition penalty parameter influence the behavior of a generative AI model, and which of the following scenarios demonstrates its correct usage?
  • A. A repetition penalty of 1.0 encourages the model to introduce more varied word choices in its output
  • B. Setting the repetition penalty to 1.0 disables any influence on repetition and allows the model to generate text without penalizing previously used tokens
  • C. A repetition penalty higher than 1.0 will discourage the model from repeating the same words or phrases, while a value lower than 1.0 encourages repetition
  • D. A repetition penalty of 0.5 is ideal for generating creative text, as it promotes repetition of key phrases for emphasis
Answer: C

NEW QUESTION # 248
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