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IBM C1000-185 Practice Exams (Web-Based & Desktop) Software

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q284-Q289):

NEW QUESTION # 284
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 = 0.5, Top-p = 0.9, Max tokens = 300, Frequency penalty = 0.8, Presence penalty = 0.7
C. Temperature = 0.7, Top-p = 0.4, Max tokens = 100, Frequency penalty = 0.9, Presence penalty = 0.3
D. Temperature = 1.5, Top-p = 0.8, Max tokens = 150, Frequency penalty = 0.5, Presence penalty = 0.6

Answer: D

NEW QUESTION # 285
You are tasked with designing a prompt using a few-shot strategy for Watsonx AI to generate a legal contract summary. You provide two examples of contract summaries before asking the model to summarize a new contract.
Which of the following options best demonstrates an effective few-shot prompt for this task?

A. "Provide a summary of the following contract: [New Contract].
Example 1: [First Contract]
Summary: [First Contract Summary]
Example 2: [Second Contract]
Summary: [Second Contract Summary]."
B. "Example 1: [First Contract] Summary: [First Contract Summary]
Example 2: [Second Contract] Summary: [Second Contract Summary]
Generate a summary of the following contract: [New Contract]."
C. "Example 1: [First Contract]
D. "Generate a summary for [New Contract]."

Answer: B

NEW QUESTION # 286
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 model, upload the dataset, and let Tuning Studio automatically generate synthetic data to improve model training.
B. Input the dataset, manually adjust the learning rate and batch size, and export the fine-tuned model without evaluation.
C. Load the model, automatically adjust its architecture, and deploy it to production.
D. Select a pre-trained model, upload the custom medical dataset, fine-tune the hyperparameters, and evaluate the model's performance.

Answer: D

NEW QUESTION # 287
You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?

A. Use a prompt that explicitly asks for neutrality across demographic groups.
B. Switch to using deterministic (greedy) decoding to ensure more consistent outputs
C. Lower the temperature to reduce randomness in the model's response.
D. Incorporate additional training data from underrepresented demographic groups.

Answer: D

NEW QUESTION # 288
A team is using IBM InstructLab to customize a large language model (LLM) to automate responses in a healthcare chatbot application. The team wants to ensure the chatbot can handle user queries accurately, based on domain-specific instructions.
Which of the following correctly describes the role of the instruction optimization phase within the InstructLab workflow?

A. Instruction optimization focuses on improving the dataset's quality by removing outliers and noise.
B. Instruction optimization refines prompts to improve the model's ability to follow task-specific instructions.
C. Instruction optimization involves retraining the model on a larger dataset for better accuracy.

Answer: B

NEW QUESTION # 289
......

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