最新のC1000-185問題集で試験を準備する

Which of the following best describes the effect of controlling model parameters during the decoding process in IBM Watsonx's generative AI models?

• A. Model parameters control the model's ability to self-learn from previous outputs, improving its performance over time.
• B. Controlling model parameters allows the model to generate only the shortest possible responses, ensuring concise outputs.
• C. Adjusting model parameters helps control the randomness in the output generation process, enabling a balance between creativity and accuracy.
• D. Setting model parameters guarantees that the model will generate the most grammatically correct response, regardless of context.

What is the key difference between zero-shot and few-shot prompting when used in generative AI models like IBM Watsonx?

• A. Zero-shot prompting does not provide any examples in the prompt, while few-shot prompting includes multiple task examples.
• B. Zero-shot prompting provides feedback to the model during inference, while few-shot does not allow model feedback.
• C. In zero-shot prompting, the model is fine-tuned before answering, but in few-shot prompting, no fine-tuning occurs.
• D. Few-shot prompting requires a model to have pre-trained examples of the task, while zero-shot does not.

You are tasked with creating a prompt template for IBM Watsonx to generate customer support responses based on user queries. The response needs to be polite, concise, and address the issue directly.
Which of the following is the most appropriate structure for a reusable prompt template to ensure consistency across multiple queries?

• A. "Please write a polite and professional response to the customer's query, including any relevant context or background information and focusing on the core issue."
• B. "Generate a professional response to the customer's query, avoiding repetition and unnecessary details, while focusing on addressing the issue succinctly."
• C. "Write a short and casual response to the customer, focusing on being friendly and engaging, regardless of the content of the query."
• D. "Generate a detailed and formal response to the customer, focusing on providing as much information as possible, even if it's unrelated to the query."

Your team has developed multiple versions of a custom Watsonx Generative AI model to address different use cases. During deployment, the client requires that all model versions be deployed concurrently, allowing requests to be routed to the appropriate model based on the input data.
What deployment strategy would best accommodate this requirement?

• A. Blue-Green deployment
• B. Canary deployment
• C. Multi-model serving with dynamic routing
• D. A/B testing deployment

You have completed a prompt-tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model.
Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt-tuning experiment?

• A. Perplexity score
• B. BLEU score
• C. Log-likelihood of generated responses
• D. Token generation speed