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You are responsible for optimizing the energy efficiency of an AI data center that handles both training and inference workloads. Recently, you have noticed that energy costs are rising, particularly during peak hours, but performance requirements are not being met. Which approach would best optimize energy usage while maintaining performance levels?

You have developed two different machine learning models to predict house prices based on various features like location, size, and number of bedrooms. Model A uses a linear regression approach, while Model B uses a random forest algorithm. You need to compare the performance of these models to determine which one is better for deployment. Which two statistical performance metrics would be most appropriate to compare the accuracy and reliability of these models? (Select two)

You are managing an AI-driven autonomous vehicle project that requires real-time decision-making and rapid processing of large data volumes from sensors like LiDAR, cameras, and radar. The AI models must run on the vehicle's onboard hardware to ensure low latency and high reliability. Which NVIDIA solutions would be most appropriate to use in this scenario? (Select two)

You are working under the supervision of a senior data scientist to analyze the results of a large-scale machine learning experiment. The experiment produced a vast amount of data, and your task is to create visualizations that effectively convey the key findings to stakeholders. The stakeholders are particularly interested in understanding the model's performance across different categories. Which type of visualization would be most appropriate for comparing the model's performance across multiple categories?

Your company is running a distributed AI application that involves real-time data ingestion from IoT devices spread across multiple locations. The AI model processing this data requires high throughput and low latency to deliver actionable insights in near real-time. Recently, the application has been experiencing intermittent delays and data loss, leading to decreased accuracy in the AI model's predictions. Which action would BEST improve the performance and reliability of the AI application in this scenario?