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New Latest C1000-185 Test Camp | High Pass-Rate IBM Reliable C1000-185 Exam Papers: IBM watsonx Generative AI Engineer - Associate

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q218-Q223):

NEW QUESTION # 218
You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A. Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
B. Implement a post-processing filter to remove any potentially offensive or legally sensitive content.
C. Use a comprehensive training dataset that includes diverse business domains to reduce biases.
D. Include fairness metrics in the model evaluation stage to monitor for biased outputs.

Answer: D

NEW QUESTION # 219
You are deploying a large language model in a financial advisory platform to assist users in making investment decisions.
Which of the following represent significant risks that should be mitigated before full deployment? (Select two)

A. The model is trained on open-source financial data, which results in slower response times during inference.
B. The model offers speculative advice without indicating the associated level of uncertainty, which may mislead inexperienced investors.
C. The model generates recommendations that align with historical financial trends but fail to account for recent economic disruptions.
D. The model provides longer-than-expected responses, potentially causing user frustration and increasing abandonment rates on the platform.
E. The model occasionally generates offensive or inappropriate content when responding to user queries.

Answer: B,C

NEW QUESTION # 220
When optimizing the tuning process in IBM watsonx Tuning Studio, what is the main purpose of fine-tuning metering options?

A. To enforce stricter thresholds for model accuracy in order to ensure high precision.
B. To limit the number of iterations allowed for model training.
C. To monitor the effectiveness of early stopping techniques applied during training.
D. To track the computational cost and time for fine-tuning models based on resource consumption.

Answer: D

NEW QUESTION # 221
When creating a prompt-tuned model, which of the following factors is most critical to ensure that the model generates accurate and contextually relevant responses?

A. Matching the training data prompts as closely as possible to real-world deployment scenarios.
B. Ensuring that the prompt length does not exceed the model's input token limit.
C. Using a diverse set of unrelated tasks in the tuning dataset to increase model generalization.
D. Employing low learning rates to prevent the model from adapting too quickly to new prompts.

Answer: A

NEW QUESTION # 222
Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?

A. Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.
B. Soft prompts make it easier to control the model's behavior as the prompts are flexible and can be adjusted by the user during inference.
C. Soft prompts can increase the model's interpretability by providing clear, user-defined input instructions.
D. Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.

Answer: D

NEW QUESTION # 223
......

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