IBM watsonx Generative AI Engineer - Associate C1000-185 Exam Questions

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Question #1 (Topic: Demo Questions)

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?

Zero-shot prompting does not use any examples in the input prompt, while few-shot prompting includes a few examples to guide the model.

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.

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.

Few-shot prompting is used only for training the model, while zero-shot prompting is used only for inference tasks.

Correct Answer: A

Question #2 (Topic: Demo Questions)

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?

Prompt variables eliminate the need to change model parameters every time you generate a new response

Prompt variables automatically improve the accuracy of responses by reducing model variance

Prompt variables ensure that the AI's response format will always be consistent, regardless of the input data

Prompt variables allow a single prompt template to handle multiple data points or scenarios by inserting different values.

Correct Answer: D

Question #3 (Topic: Demo Questions)

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)

Prompt variables improve the model's performance by optimizing its internal architecture, reducing computation time for each request.

Prompt variables reduce redundancy by allowing dynamic inputs to be injected into a single prompt template, improving scalability.

Using prompt variables allows the model to dynamically adjust its output based on context, without requiring multiple task-specific prompts

Prompt variables eliminate the need for fine-tuning the model on specific tasks since they allow onthe-fly customization of responses

Prompt variables require a complete re-training of the model whenever a new variable is introduced, which can be time-consuming.

Correct Answer: B, C

Question #4 (Topic: Demo Questions)

You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model. Which of the following statements best describes the key difference between zero-shot and few-shot prompting?

Zero-shot prompting provides the model with examples, while few-shot prompting does not.

Zero-shot prompting requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task

Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models

Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining

Correct Answer: B

Question #5 (Topic: Demo Questions)

You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination. Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?

Implementing zero-shot learning techniques

Providing a list of credible sources in the prompt

Using more open-ended prompts

Increasing the model's temperature parameter

Correct Answer: B

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