IBM C1000-185 問題集 練習問題・模擬試験｜日本語版＆英語版対応

サンプル問題 1： In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few-shot prompting techniques. Which of the following best explains the key difference between zero-shot and few-shot prompting?

• A: Zero-shot prompting does not use any examples in the input prompt, while few-shot prompting includes a few examples to guide the model.
• B: Zero-shot prompting provides the model with a few example tasks to help it understand the problem, while few-shot prompting provides no examples at all.
• C: In zero-shot prompting, the model learns from a large number of examples during the inference stage, while in few-shot prompting, only a single example is used.
• D: Few-shot prompting is used only for training the model, while zero-shot prompting is used only for inference tasks.

サンプル問題 2： In prompt engineering, prompt variables are used to make your prompts more dynamic and reusable. Which of the following statements best describes a key benefit of using prompt variables in IBM Watsonx Generative AI?

• A: Prompt variables eliminate the need to change model parameters every time you generate a new response.
• B: Prompt variables automatically improve the accuracy of responses by reducing model variance.
• C: Prompt variables ensure that the AI's response format will always be consistent, regardless of the input data.
• D: Prompt variables allow a single prompt template to handle multiple data points or scenarios by inserting different values.

サンプル問題 3： You are working on a project where the AI model needs to generate personalized customer support responses based on various input fields like customer name, issue type, and product details. To make the system scalable and flexible, you decide to use prompt variables in your implementation. Which of the following statements accurately describe the benefits of using prompt variables in this scenario? (Select two)

• A: Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.
• B: Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.
• C: Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts.
• D: Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow on-the-fly customization of responses.
• E: Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.