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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with generating a product description for an e-commerce platform using a generative AI model. However, you notice that the generated text tends to repeat phrases excessively, leading to verbose output. To address this, you decide to adjust the model's temperature parameter.
Which of the following changes would help reduce the repetitiveness of the generated text while maintaining a balance between creativity and coherence?

A) Set the temperature to 0.0
B) Decrease the temperature from 0.8 to 0.6
C) Increase the temperature from 0.5 to 1.5
D) Decrease the temperature from 0.9 to 0.3

2. You are tasked with deploying a generative AI solution for a client who operates in the healthcare sector. Due to the sensitive nature of the data, the client requires a highly secure deployment with continuous monitoring for regulatory compliance.
Which role is primarily responsible for ensuring the AI solution is compliant with these security and regulatory requirements?

A) Data Privacy Officer
B) Security Engineer
C) Chief Technology Officer (CTO)
D) AI Model Developer

3. When using Tuning Studio in IBM watsonx, which of the following types of models is most suitable for fine-tuning in a domain-specific task to optimize performance?

A) A model trained from scratch on a small, specific dataset.
B) A pre-trained model with very few layers to ensure fast processing speeds.
C) A pre-trained model that has been trained on a large, general-purpose dataset.
D) An untrained model with a minimal number of layers to reduce complexity.

4. You are optimizing a large language model (LLM) by prompt-tuning it for specific enterprise-level tasks. The goal is to initialize the prompt in such a way that it helps the model generalize well across various enterprise domains, such as finance, healthcare, and retail.
What is the most effective method to initialize the prompt for such a use case?

A) Initialize the prompt with an ensemble of prompts covering multiple domains
B) Use a single, highly specific prompt tailored to only one domain, such as finance
C) Use a short prompt that provides no guidance and allow the model to self-optimize
D) Start with a general prompt and gradually specialize it during fine-tuning

5. In which of the following scenarios would zero-shot prompting be more effective than few-shot prompting when interacting with a generative AI model?

A) When the task requires highly domain-specific knowledge that the model has not been exposed to before.
B) When the model is expected to perform a novel task it has never seen, but the prompt can include several examples for guidance.
C) When the goal is to adjust the model's response based on few labeled examples that help refine its predictions.
D) When the prompt is designed for a general task like summarizing a text, which the model is pre-trained on.

Solutions: