You are assisting a professional administrator in ensuring data integrity during AI model training in an AI data center. Which of the following strategies would best contribute to maintaining data integrity across distributed GPU nodes?

A financial institution is implementing a real-time fraud detection system using deep learning models. The system needs to process large volumes of transactions with very low latency to identify fraudulent activities immediately. During testing, the team observes that the system occasionally misses fraudulent transactions under heavy load, and latency spikes occur. Which strategy would best improve the system's performance and reliability?

You are managing an AI infrastructure setup where multiple GPUs are used to accelerate deep learning workloads. Suddenly, one of the nodes in the GPU cluster becomes unresponsive, leading to a significant drop in training performance. What should be your first course of action to troubleshoot the issue?

In collaboration with a professional administrator, you are tasked with optimizing power efficiency in an AI data center that runs multiple deep learning workloads. Which of the following actions would most effectively contribute to reducing power consumption without sacrificing performance?

You are part of a team investigating the performance variability of an AI model across different hardware configurations. The model is deployed on various servers with differing GPU types, memory sizes, and CPU clock speeds. Your task is to identify which hardware factors most significantly impact the model's inference time. Which analysis approach would be most effective in identifying the hardware factors that significantly impact the model’s inference time?