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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with fine-tuning a generative AI model for text data using synthetic data created through the IBM watsonx platform's user interface. The data you are working with is skewed, containing mostly outliers, and you need to ensure that the synthetic data mimics the distribution accurately.
Which algorithm would be most appropriate for generating synthetic data that mirrors the original distribution, considering the Anderson-Darling test for normality?

A) Anderson-Darling Based Synthetic Data Generation (ADS-DG)
B) Bootstrapping
C) Decision Trees
D) Generative Adversarial Networks (GANs)

2. You are developing a generative AI model using the IBM Watsonx platform to assist in customer service. While the model's responses are highly accurate, there is concern that the model may inadvertently expose personal information (PII) or sensitive data during interactions. As a responsible AI engineer, it is crucial to mitigate this risk.
Which of the following is the most critical risk associated with the exposure of personal information in generative AI models?

A) The model might produce content that doesn't align with the cultural preferences of the user.
B) The model can unintentionally memorize and regurgitate personal information from the training data, leading to privacy violations.
C) The model can generate outputs that are too general, failing to meet the specific needs of the user.
D) The model can generate overly creative or non-factual responses, leading to brand reputation damage.

3. Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?

A) Soft prompts make it easier to control the model's behavior as the prompts are flexible and can be adjusted by the user during inference.
B) Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.
C) Soft prompts can increase the model's interpretability by providing clear, user-defined input instructions.
D) Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.

4. You are tasked with explaining the outcomes produced by a Watsonx Generative AI model based on specific prompts.
Which of the following approaches is most effective in ensuring transparency and understanding of how the model arrives at its decisions?

A) Providing an interpretable
B) Explaining the optimization process that minimized the model's loss function

5. You've conducted a prompt-tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies.
What is the most appropriate action to address these data quality problems?

A) Increase the length of the input prompt to ensure that responses are more complete.
B) Fine-tune the model on domain-specific data to improve factual accuracy and relevance.
C) Lower the model's perplexity score to improve both completeness and factual accuracy.
D) Introduce temperature tuning to adjust the randomness of the model's output and reduce irrelevant content.

Solutions: