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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q300-Q305):

NEW QUESTION # 300
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. An untrained model with a minimal number of layers to reduce complexity.
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. A model trained from scratch on a small, specific dataset.

Answer: C

NEW QUESTION # 301
You are optimizing a Generative AI model for a business application where cost savings are a priority.
Which of the following modifications to the model's parameters will most effectively reduce the overall generation cost while minimizing the loss of output quality?

A. Decrease the model's context window size.
B. Set a lower value for the frequency penalty parameter.
C. Set a lower value for the top-p (nucleus sampling) parameter.
D. Reduce the number of layers in the neural network during inference.

Answer: C

NEW QUESTION # 302
You are using IBM's Tuning Studio to fine-tune a large-scale foundation model for a customer service chatbot. The goal is to optimize the model for performance in handling a wide variety of customer queries while minimizing computational costs. Before making any changes, you want to understand how Tuning Studio can help achieve your optimization goals.
Which of the following is the most significant benefit provided by Tuning Studio when optimizing a generative AI model?

A. Tuning Studio provides real-time monitoring of model performance metrics during the fine-tuning process, allowing you to adjust hyperparameters effectively.
B. Tuning Studio allows the user to implement custom model architectures from scratch to meet specific task requirements.
C. Tuning Studio reduces the dataset size needed for training by implementing automated data augmentation strategies.
D. Tuning Studio automatically deploys the fine-tuned model to production environments without requiring further testing.

Answer: A

NEW QUESTION # 303
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.

Answer: B

NEW QUESTION # 304
While working on a fine-tuning project in IBM watsonx, you need to generate synthetic data that mimics the properties of your existing dataset for training purposes. You have two algorithms available: Algorithm A (Kolmogorov-Smirnov Test) and Algorithm B, which uses a different methodology for assessing similarity between original and synthetic data.
After generating the synthetic data using the User Interface, what would be the primary consideration in choosing the correct algorithm to validate that the generated data sufficiently mimics the original data?

A. Choose Algorithm A (Kolmogorov-Smirnov Test) if the synthetic data has categorical variables, as this test is specifically designed for discrete distributions.
B. Choose Algorithm A (Kolmogorov-Smirnov Test) if you want to ensure that the original and synthetic data distributions are identical across their entire range, not just at specific points.
C. Use Algorithm B if you want to focus on minimizing the difference in mean squared error (MSE) between original and synthetic data distributions.
D. Use Algorithm B to compare the entropy of the original and synthetic data distributions, ensuring both data sets have the same level of uncertainty.

Answer: B

NEW QUESTION # 305
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