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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

When conducting prompt engineering to reduce model risks related to hate speech and abusive content, which of the following strategies is least likely to be effective?

A. Increasing the temperature during text generation to encourage more creative outputs
B. Modifying the prompt to include explicit instructions for civil language
C. Creating a reward model during Reinforcement Learning to penalize hate speech outputs
D. Applying ethical guidelines during the fine-tuning of the model to prioritize inclusive language

Question 2

You are working on deploying a generative AI model into production. The goal is to ensure that different versions of prompts can be tracked and rolled back in case of degradation in the model's performance.
Which of the following strategies would best address versioning for deployment?

A. Integrate prompt versioning into the model deployment pipeline using automated versioning tools like Git.
B. Maintain different versions of the model and prompts by duplicating them across multiple endpoints without a centralized repository.
C. Use manual version control through logging and local storage.
D. Use endpoint monitoring tools only, without any versioning approach, to track and assess prompt changes in production.

Question 3

In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?

A. Retraining the model based on emerging trends in data
B. Initial testing on synthetic datasets and model validation
C. Iterative prompt tuning based on A/B test results and feedback loops
D. Deployment to production with regular monitoring and logging

Question 4

You are tasked with building a generative AI model to help create automated marketing copy for a business. A key concern is the potential generation of biased or legally sensitive content, which could negatively impact the company's reputation.
Which of the following strategies would be the most effective in mitigating these model risks?

A. Use reinforcement learning to fine-tune the model based on user feedback to eliminate bias in the long term.
B. Include fairness metrics in the model evaluation stage to monitor for biased outputs.
C. Use a comprehensive training dataset that includes diverse business domains to reduce biases.
D. Implement a post-processing filter to remove any potentially offensive or legally sensitive content.

Question 5

You are working on a generative AI model for a wide variety of language generation tasks. Your team is debating whether to use soft prompts for optimizing the model's performance.
Which of the following is an accurate benefit of using soft prompts, and what is a potential drawback?

A. Benefit: Soft prompts can optimize performance without retraining the model. Drawback: They are computationally expensive to tune during inference.
B. Benefit: Soft prompts offer more control over the output. Drawback: Soft prompts require additional manual engineering for each task.
C. Benefit: Soft prompts reduce the amount of explicit data needed for training. Drawback: Soft prompts might not generalize well across domains.
D. Benefit: Soft prompts allow for fine-grained control during inference. Drawback: Soft prompts may lead to lower performance with large datasets.

Solutions: