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2025 New C1000-185 Dumps Free | Professional IBM C1000-185 Test Review: IBM watsonx Generative AI Engineer - Associate

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q114-Q119):

NEW QUESTION # 114
You are tasked with deploying a custom prompt template in an enterprise environment.
What is the most critical first step in defining the deployment lifecycle to meet client needs?

A. Establish a model selection strategy for each prompt template
B. Identify the operational requirements and business constraints
C. Deploy the prompt template directly to production to get rapid feedback
D. Define the monitoring and feedback mechanisms for the prompt's performance

Answer: B

NEW QUESTION # 115
You are tasked with fine-tuning a generative AI model for text data using synthetic data created through the IBM watsonx platform's user interface. The data you are working with is skewed, containing mostly outliers, and you need to ensure that the synthetic data mimics the distribution accurately.
Which algorithm would be most appropriate for generating synthetic data that mirrors the original distribution, considering the Anderson-Darling test for normality?

A. Generative Adversarial Networks (GANs)
B. Bootstrapping
C. Anderson-Darling Based Synthetic Data Generation (ADS-DG)
D. Decision Trees

Answer: C

NEW QUESTION # 116
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. Perplexity score; apply additional language model fine-tuning on grammatical correctness.
B. BLEU score; improve prompt engineering to ensure that the model focuses on fluency.
C. ROUGE score; adjust the token generation limit to ensure longer outputs.
D. F1 score; increase the training dataset size to improve overall accuracy.

Answer: A

NEW QUESTION # 117
In the context of using Tuning Studio in IBM watsonx, which of the following is the primary benefit of fine-tuning a pre-trained Generative AI model using this tool?

A. Allows you to train the model from scratch on a new dataset.
B. Increases the size of the model to handle more complex prompts.
C. Improves model performance on domain-specific tasks without retraining from scratch.
D. Reduces the time needed to generate text responses by simplifying the model's architecture.

Answer: C

NEW QUESTION # 118
Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?

A. Prompt variables ensure that the same text input will always yield the same output, improving consistency.
B. Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
C. Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
D. Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.

Answer: C

NEW QUESTION # 119
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