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C1000-185 Latest Exam Fee & C1000-185 Latest Version

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q172-Q177):

NEW QUESTION # 172
You are tasked with optimizing a generative AI model's output for a natural language generation task.
Which of the following combinations of model parameters is most appropriate for encouraging creative and varied responses without sacrificing too much coherence?

A. Temperature = 1.2, Top-p = 1.0, Max tokens = 250, Frequency penalty = 0.3, Presence penalty = 0.2
B. Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6
C. Temperature = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
D. Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3

Answer: B

NEW QUESTION # 173
In the context of Tuning Studio in IBM watsonx, what is one of the key benefits of using Compute Unit Hours (CUHs) during the fine-tuning process?

A. It reduces the time required to train models by lowering the accuracy threshold.
B. It limits the number of model versions stored, improving system performance.
C. It allows for the precise allocation of computational resources to manage budget constraints.
D. It provides real-time feedback on model deployment success rates.

Answer: C

NEW QUESTION # 174
You are fine-tuning a generative AI model using Tuning Studio for a legal document analysis task. The model needs to perform well in summarizing long, complex legal texts while minimizing the amount of computational resources used. You are tasked with optimizing the tuning process to ensure maximum efficiency and model accuracy.
Which of the following actions would most effectively optimize the tuning process in Tuning Studio for this task?

A. Adjusting the learning rate dynamically during training based on the model's real-time loss values.
B. Using a fixed number of training epochs to ensure consistent results across tuning iterations.
C. Reducing the number of layers in the model to decrease computational overhead.
D. Increasing the batch size to reduce the number of gradient updates during training, thus speeding up the process.

Answer: A

NEW QUESTION # 175
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. Ensuring that the prompt length does not exceed the model's input token limit.
B. Using a diverse set of unrelated tasks in the tuning dataset to increase model generalization.
C. Employing low learning rates to prevent the model from adapting too quickly to new prompts.
D. Matching the training data prompts as closely as possible to real-world deployment scenarios.

Answer: D

NEW QUESTION # 176
You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

A. Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
B. Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.
C. Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.
D. Store all prompt versions directly in the model's code repository, updating the main branch with each new version.

Answer: B

NEW QUESTION # 177
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