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[Hardware] Free PDF Quiz 2026 IBM C1000-185: Marvelous IBM watsonx Generative AI Engineer -

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【Hardware】 Free PDF Quiz 2026 IBM C1000-185: Marvelous IBM watsonx Generative AI Engineer -

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q210-Q215):NEW QUESTION # 210
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. Increasing the model's learning rate
  • B. Removing weight decay entirely
  • C. Reducing the model's learning rate
  • D. Increasing the model's dropout rate
Answer: D

NEW QUESTION # 211
You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)
  • A. Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
  • B. Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
  • C. Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.
  • D. Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
  • E. Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.
Answer: B,E

NEW QUESTION # 212
You are using a generative AI model in a healthcare application to generate personalized treatment recommendations based on patient data.
Which of the following scenarios represent valid concerns related to model risks when deploying the AI in this setting? (Select two)
  • A. The model produces highly creative treatment recommendations that are not based on standard medical guidelines.
  • B. The model fails to generate treatment recommendations for some patients due to exceeding token limits during inference.
  • C. The model includes probabilistic estimates for treatment outcomes, which adds uncertainty to the recommendations and reduces their usability.
  • D. The model generates text that includes private patient information, violating data privacy regulations.
  • E. The model generates biased recommendations based on incomplete or skewed training data, which disproportionately impacts certain patient demographics.
Answer: D,E

NEW QUESTION # 213
In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?
  • A. Iterative prompt tuning based on A/B test results and feedback loops
  • B. Initial testing on synthetic datasets and model validation
  • C. Retraining the model based on emerging trends in data
  • D. Deployment to production with regular monitoring and logging
Answer: A

NEW QUESTION # 214
In the context of Generative AI models, which scenario best illustrates the use of greedy decoding for determining the output sequence?
  • A. Selecting the token that maximizes the cumulative probability of the sequence, considering future tokens
  • B. Selecting the token with the highest probability at each step, without considering future possibilities
  • C. Randomly selecting tokens from the top-k highest probability tokens at each step
  • D. Averaging the probabilities of multiple tokens at each step to generate a sequence
Answer: B

NEW QUESTION # 215
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