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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

A) Use a deployment space in IBM watsonx to version your prompts, assigning unique tags to each version and ensuring rollback capabilities.
B) Embed the prompt version directly into the API request body so that the deployed model can select the correct prompt dynamically at runtime.
C) Keep prompt versions in an external document management system and manually track which versions are deployed in the application.
D) Store all prompt versions directly in the model's code repository, updating the main branch with each new version.

2. You are tasked with generating high-quality responses from a large language model for a customer support application. You want to minimize the amount of provided examples while ensuring that the model generates relevant and specific answers.
Which of the following statements best differentiates between zero-shot and few-shot prompting in this context? (Select two)

A) Few-shot prompting improves model performance for unfamiliar tasks by fine-tuning weights based on examples, while zero-shot prompting leaves the model weights unchanged.
B) Zero-shot prompting is better suited for tasks requiring domain-specific knowledge, while few-shot prompting is better for general knowledge tasks.
C) In zero-shot prompting, the model's response is generated purely based on pre-trained knowledge and the structure of the task, while in few-shot prompting, the examples provided offer the model additional context.
D) ct selection
E) Zero-shot prompting does not require any examples in the input prompt, while few-shot prompting uses a limited number of examples to guide the model's response.
F) Few-shot prompting involves fine-tuning the model on a specific dataset before generating output, whereas zero-shot prompting uses pre-trained knowledge without additional fine-tuning.

3. You are tasked with integrating watsonx.ai into a legacy system that operates over HTTP and requires strict security and monitoring of the API calls. The legacy system lacks modern authentication mechanisms like OAuth.
Which integration method would best suit the needs of this environment while ensuring security and efficient API management?

A) Use a REST API with OAuth authentication and token management to secure the communication.
B) Implement a GraphQL API to allow the legacy system to query specific fields and reduce payload size, thereby improving efficiency.
C) Directly embed the watsonx.ai SDK into the legacy system to handle API management and security, eliminating the need for external API calls.
D) Use REST API with API key-based authentication, leveraging HTTPS for encrypted communication between the legacy system and watsonx.ai.

4. When optimizing a generative AI model using the Tuning Studio in IBM Watsonx, which two of the following actions can most effectively improve model performance when dealing with underfitting issues? (Select two)

A) Decrease the batch size
B) Increase the model's complexity by adding more layers
C) Increase the number of training epochs
D) Enable early stopping
E) Reduce the learning rate

5. In a generative AI-based customer service chatbot, you notice that the model sometimes generates user responses that inadvertently reveal sensitive personal information, such as names, addresses, or social security numbers.
What is the most effective prompt engineering technique to reduce this risk while preserving the chatbot's functionality?

A) Reduce the model's token limit to restrict the amount of text generated
B) Increase the model's randomness by adjusting the temperature parameter
C) Use generic prompts that include placeholders for sensitive information (e.g., [USER_NAME])
D) Introduce explicit instructions in the prompt to avoid generating personal information

Solutions: