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Free PDF 2025 IBM Efficient New C1000-185 Test Prep

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q260-Q265):

NEW QUESTION # 260
You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

A. The model can generate overly creative or non-factual responses, leading to brand reputation damage.
B. The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
C. The model might produce content that doesn't align with the cultural preferences of the user.
D. The model can generate outputs that are too general, failing to meet the specific needs of the user.

Answer: B

NEW QUESTION # 261
When analyzing the results of a prompt tuning experiment, which two of the following actions are most appropriate if you observe a consistently high variance in model predictions across different prompt templates? (Select two)

A. Add more layers to the model to increase complexity
B. Enable regularization techniques like dropout
C. Increase the number of training samples used for tuning
D. Increase the batch size during training
E. Tune the prompt templates further by standardizing the structure

Answer: C,E

NEW QUESTION # 262
You are developing a tuned language model for a healthcare chatbot that provides concise responses to patient inquiries. Using Tuning Studio, you want to ensure the model is well-optimized for generating responses specific to medical terminology while maintaining efficiency.
Which of the following represents the correct workflow to create a tuned model using Tuning Studio?

A. Input the dataset, manually adjust the learning rate and batch size, and export the fine-tuned model without evaluation.
B. Select a pre-trained model, upload the custom medical dataset, fine-tune the hyperparameters, and evaluate the model's performance.
C. Load the model, automatically adjust its architecture, and deploy it to production.
D. Select a model, upload the dataset, and let Tuning Studio automatically generate synthetic data to improve model training.

Answer: B

NEW QUESTION # 263
What is the key difference between zero-shot and few-shot prompting when used in generative AI models like IBM Watsonx?

A. Few-shot prompting requires a model to have pre-trained examples of the task, while zero-shot does not.
B. In zero-shot prompting, the model is fine-tuned before answering, but in few-shot prompting, no fine-tuning occurs.
C. Zero-shot prompting provides feedback to the model during inference, while few-shot does not allow model feedback.
D. Zero-shot prompting does not provide any examples in the prompt, while few-shot prompting includes multiple task examples.

Answer: D

NEW QUESTION # 264
You are tasked with designing a prompt template to assist a chatbot in generating professional email responses for customer service inquiries. The system should prioritize politeness, clarity, and conciseness.
What elements should be included in the prompt template to achieve the best results, considering optimal behavior of a large language model (LLM)? (Select two)

A. Specify the output tone as polite and professional
B. Ask the model to generate multiple versions of the response and rank them
C. Instruct the model to limit responses to a specific character count
D. Provide the customer's emotional context for better alignment with the tone
E. Include examples of informal customer service responses for variability

Answer: A,C

NEW QUESTION # 265
......

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