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1. When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?

A) Training the model on a diverse and representative dataset
B) Incorporating fairness constraints during the model's training phase
C) Using temperature control during generation to manage diversity in responses
D) Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases

2. You are working with IBM Watsonx and need to generate synthetic data to improve your model's performance on a custom domain-specific task. After importing a dataset, you want to use the User Interface to generate this synthetic data.
What is the primary benefit of using synthetic data generation in fine-tuning your model?

A) It automatically anonymizes sensitive data points to comply with data privacy regulations during the synthetic data generation process.
B) It eliminates the need for any human intervention in the fine-tuning process.
C) It improves the model's generalization by exposing it to a wider variety of data points and scenarios.
D) It creates a larger training dataset by duplicating and randomizing the existing data, which enhances model accuracy.

3. You are integrating watsonx.ai into an external system to handle text generation for a content creation application. The external system requires real-time processing and needs to interact with watsonx.ai frequently. Given this requirement, which integration method is most appropriate for ensuring reliable and scalable communication between the external system and watsonx.ai?

A) Use a REST API with synchronous requests, where the external system waits for watsonx.ai to respond before proceeding.
B) Leverage asynchronous REST API calls with callbacks to enable the external system to send requests and continue processing while waiting for the response.
C) Integrate watsonx.ai through the SDK to directly embed AI capabilities into the external system, eliminating the need for API calls.
D) Implement Webhooks to receive updates from watsonx.ai when new data is generated and push it to the external system.

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

5. While working with IBM Watsonx, you are asked to ensure that the synthetic data generated has a low privacy leakage probability. During the generation process, you must adjust settings related to privacy leakage probability.
Which factor most directly affects the probability of privacy leakage when generating synthetic data with differential privacy?

A) The size of the original dataset used for training.
B) The use of ensemble models to train on different portions of the dataset and reduce dependency on individual records.
C) The amount of noise added to the synthetic data during the differential privacy process.
D) The quality and variance of the synthetic data generated.

問題與答案：