You are responsible for deploying an LLM in a customer service application. The LLM should effectively manage and prioritize different types of customer queries, such as urgent complaints, general inquiries, and feedback. However, during testing, you observe that the model struggles to prioritize urgent complaints appropriately, treating them with the same priority as general inquiries. The senior team member suggests adjusting the training process to improve the model's ability to handle priorities. Which strategy is most likely to improve the model’s ability to prioritize urgent complaints?

You have trained two different generative AI models, Model X and Model Y, on the same dataset. To compare their performance, you calculate the Mean Squared Error (MSE) and the proportion of explained variance (R²). Model X has a lower MSE but also a lower R² compared to Model Y. Which model is likely better at explaining the variability in the data?

You are working on a regression problem to predict house prices based on several features, including the number of bedrooms, square footage, and neighborhood quality (categorical). Which combination of Python packages and methods should you use to prepare the dataset for a Linear Regression model?

You are working on deploying a generative AI model that creates personalized email content for a large marketing campaign. The model needs to handle high traffic and maintain performance as the number of users increases. Under the supervision of a senior team member, you need to evaluate the model’s scalability and performance. Which of the following actions would best help you ensure that the model can scale effectively while maintaining performance? (Select two)

Which of the following practices is the most critical in ensuring that a generative AI system used for content creation is trustworthy, ethical, and energy-efficient?