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When using IBM Watsonx Tuning Studio, what is the recommended approach to determining the number of training data examples required for effective model fine-tuning?

• A. Use a minimum of 1,000 to 5,000 examples for each task, but focus on the quality and relevance of examples rather than quantity.
• B. Use at least 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.
• C. Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.
• D. Use at least 10,000 examples for each unique task to ensure the model retains its general knowledge and effectively adapts to the new task.

In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

• A. Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.
• B. Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.
• C. Top-k sampling selects the token with the highest probability, ignoring all other token options.
• D. Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.

Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?

• A. Temperature Sampling with temperature = 0.0
• B. Nucleus Sampling (Top-p) with p = 0.9
• C. Beam Search Decoding with a small beam size (e.g., 2)
• D. Greedy Decoding

In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)

• A. "for"
• B. "[customer_name]"
• C. "based on their interest"
• D. "Recommend products"
• E. "[product_category]"

You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses.
Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?

• A. Splitting the prompt into multiple sub-prompts to generate responses for different sections separately.
• B. Revising the prompt to make it more specific by narrowing the scope of expected responses.
• C. Using temperature scaling to increase randomness and reduce token usage.
• D. Reducing the token limit in the model configuration to restrict the length of responses.