Updated C1000-185 Practice Exams for Self-Assessment (Web-Based )

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q261-Q266):

NEW QUESTION # 261
When setting up a tuning experiment in IBM watsonx's Tuning Studio, which of the following best describes the process for optimizing a model's hyperparameters?

A. Manually adjust one hyperparameter at a time while keeping all other parameters constant to precisely identify its impact on performance.
B. Set the learning rate to its maximum value to speed up the tuning process and reduce experimentation time.
C. Use automated hyperparameter search techniques such as grid search or random search to explore multiple configurations efficiently.
D. All hyperparameters should be fixed at the default settings to ensure consistency across different experiments.

Answer: C

NEW QUESTION # 262
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. Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.
C. Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
D. Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.

Answer: C

NEW QUESTION # 263
You are tasked with optimizing a prompt-tuned large language model (LLM) using IBM Watsonx for a customer service chatbot. The chatbot needs to handle a variety of tasks, such as answering frequently asked questions (FAQs), providing detailed product descriptions, and troubleshooting user issues.
What is the most appropriate task to focus on during the initial tuning experiment?

A. Fine-tune the model to generate product descriptions using longer contextual prompts.
B. Optimize the model for extractive question-answering from a predefined knowledge base.
C. Tune the model for text summarization, condensing user queries into shorter forms.
D. Focus on prompt-tuning the model for multi-turn dialogue to simulate more natural conversations.

Answer: B

NEW QUESTION # 264
You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

A. Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.
B. Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
C. Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
D. Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.

Answer: D

NEW QUESTION # 265
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. Select a pre-trained model, upload the custom medical dataset, fine-tune the hyperparameters, and evaluate the model's performance.
B. Select a model, upload the dataset, and let Tuning Studio automatically generate synthetic data to improve model training.
C. Input the dataset, manually adjust the learning rate and batch size, and export the fine-tuned model without evaluation.
D. Load the model, automatically adjust its architecture, and deploy it to production.

Answer: A

NEW QUESTION # 266
......

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Updated C1000-185 Practice Exams for Self-Assessment (Web-Based )

Three Best Formats of IBM C1000-185 Practice Test Questions

IBM watsonx Generative AI Engineer - Associate Sample Questions (Q261-Q266):

High-quality IBM Brain C1000-185 Exam Are Leading Materials & Free PDF Most C1000-185 Reliable Questions

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