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IBM C1000-185 Exam Overview:

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IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?

A. Use a diverse set of prompts covering multiple task domains with varying levels of complexity.
B. Focus on generating prompts specific to a single domain to train the model on specialized tasks.
C. Generate a single highly-detailed prompt that covers all potential use cases to maximize generalization.
D. Prioritize prompts with repetitive patterns to help the model memorize key responses.

Question 2

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. 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.
C. Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race.
D. Manually review all loan decisions generated by the model for signs of bias before releasing them to customers.

Question 3

You are fine-tuning a machine learning model using IBM Watsonx with a dataset that includes sensitive information. You decide to enable differential privacy while generating synthetic data to ensure the privacy of individual records.
What key feature of differential privacy ensures that the synthetic data does not leak private information from the original dataset?

A. Adding controlled noise to the data, ensuring that no individual's data point is easily distinguishable from aggregate data.
B. Limiting the number of data points generated to avoid overfitting the synthetic data.
C. Using clustering techniques to group similar data points, preventing individual-level data from being exposed.
D. Masking sensitive data fields before creating the synthetic data, ensuring no private information is directly used.

Question 4

You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?

A. Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
B. Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
C. Increase the temperature parameter to introduce more diversity and creativity into the model's output.
D. Set the minimum token length high to ensure the model has enough time to fully develop its response.

Question 5

In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A. When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.
B. When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.
C. When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
D. When the model needs to generate a strictly factual output with minimal deviation from the prompt.

Solutions: