You are tasked with deploying an AI model across multiple cloud providers, each using NVIDIA GPUs.
During the deployment, you observe that the model's performance varies significantly between the providers, even though identical instance types and configurations are used. What is the most likely reason for this discrepancy?
In an AI cluster, what is the purpose of job scheduling?
In your AI data center, you need to ensure continuous performance and reliability across all operations. Which two strategies are most critical for effective monitoring? (Select two)
Your AI team is deploying a large-scale inference service that must process real-time data 24/7. Given the high availability requirements and the need to minimize energy consumption, which approach would best balance these objectives?
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?
