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You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency. Which of the following networking technologies is most suitable for this scenario?

You are managing an AI training workload that requires high availability and minimal latency. The data is stored across multiple geographically dispersed data centers, and the compute resources are provided by a mix of on-premises GPUs and cloud-based instances. The model training has been experiencing inconsistent performance, with significant fluctuations in processing time and unexpected downtime. Which of the following strategies is MOST effective in improving the consistency and reliability of the AI training process?

You are designing a data center platform for a large-scale AI deployment that must handle unpredictable spikes in demand for both training and inference workloads. The goal is to ensure that the platform can scale efficiently without significant downtime or performance degradation. Which strategy would best achieve this goal?

You are managing an AI infrastructure that includes multiple NVIDIA GPUs across various virtual machines (VMs) in a cloud environment. One of the VMs is consistently underperforming compared to others, even though it has the same GPU allocation and is running similar workloads. What is the most likely cause of the underperformance in this virtual machine?

You are working on a regression task to predict car prices. Model Gamma has a Mean Absolute Error (MAE) of $1,200, while Model Delta has a Mean Absolute Error (MAE) of $1,500. Which model should be preferred based on the Mean Absolute Error (MAE), and what does this metric indicate?