IBM watsonx Generative AI Engineer - Associate - C1000-185

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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are designing prompt templates for an automated content generation tool that your team uses for marketing copy. The tool must balance cost efficiency with the generation of high-quality outputs. The prompts should be structured to avoid excessive token usage while ensuring that the model provides coherent and actionable content.
Which of the following techniques would best optimize the design of cost-effective prompt templates?

A) Creating templates that provide extensive background information to give the model context.
B) Designing prompts that incorporate redundancy to ensure model outputs address all possible user needs.
C) Using placeholders in the prompt templates for dynamic insertion of variables specific to each task.
D) Embedding explicit stop words within the prompt to control the length of generated outputs.

2. 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?

A) Implementing zero-shot learning techniques
B) Providing a list of credible sources in the prompt
C) Increasing the model's temperature parameter
D) Using more open-ended prompts

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

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

4. You are working with IBM Watsonx and have developed a custom machine learning model using Watson Studio. Now, you need to deploy the model so that it can be accessed via an application.
Which step is NOT required when deploying the custom model for application access?

A) Registering the model in Watson Machine Learning (WML)
B) Defining a scoring endpoint for the model in Watson Machine Learning
C) Verifying model accuracy before deployment.
D) Assigning appropriate IAM roles for users who need access to the model

5. 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?

A) Zero-shot prompting provides the model with examples, while few-shot prompting does not.
B) Zero-shot prompting requires retraining the model with additional data, while few-shot prompting uses a pre-trained model without retraining.
C) Few-shot prompting is used when the model is trained on supervised learning, while zero-shot prompting works only with unsupervised models.
D) 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.

Solutions: