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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A financial institution is deploying a generative AI model to generate loan approval recommendations based on applicant profiles, including factors like income, credit score, and employment history. The organization is concerned about ensuring that the model does not introduce bias in its recommendations, particularly related to gender and race. You have been asked to design a process to evaluate the model's inferences during deployment and mitigate any potential bias.
Which method would be most effective for evaluating the model's inferences for bias in this deployment scenario?

A) Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods.
B) Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race.
C) Manually review all loan decisions generated by the model for signs of bias before releasing them to customers.
D) Implement a fairness audit, where a sample of the model's inferences is checked for disparate impact across protected groups such as gender and race.

2. In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?

A) Avoiding any post-processing or filtering of synthetic data to retain diversity
B) Randomly generating sentences without adhering to the task-specific instructions
C) Using domain-specific templates to generate synthetic data that reflects the target use case
D) Generating synthetic data that overrepresents the simplest task cases to reduce computational load

3. In the context of generative AI optimization, you are tasked with improving the model's response accuracy across different domains. One suggestion is to use soft prompts for enhanced performance.
How does a soft prompt differ from a hard prompt in terms of implementation and flexibility?

A) Soft prompts can dynamically change during inference based on the input data, whereas hard prompts are fixed throughout the entire generation process.
B) Soft prompts use a rule-based system to guide the model, while hard prompts depend on statistical techniques to generate responses.
C) Hard prompts are stored as vector embeddings within the model, while soft prompts are manually crafted by users to optimize generation.
D) Soft prompts are embedded during the model's training and rely on learned representations, while hard prompts are explicit text-based inputs given during inference.

4. 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 requires no examples in the prompt, while few-shot prompting provides the model with one or more examples to clarify the task.
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 provides the model with examples, while few-shot prompting does not.

5. You are tasked with generating creative text outputs using an AI language model for a marketing campaign. You want to ensure that the responses are diverse and unexpected but still somewhat relevant to the prompt.
Which combination of temperature and random seed should you use to achieve this?

A) Temperature: 0.7, Random Seed: None
B) Temperature: 1.5, Random Seed: 123
C) Temperature: 0.1, Random Seed: 42
D) Temperature: 1.0, Random Seed: 0

Solutions: