Your question is Heterogeneous Cluster Operations. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you build and maintain heterogeneous clusters on-premises and in the cloud for NVIDIA ML workloads?
Explain how you would provision GPU and CPU nodes, standardize NVIDIA software environments, orchestrate data and training pipelines, and manage configuration drift across NVIDIA DGX systems, NVIDIA DGX SuperPOD environments, and cloud GPU instances. Cover automation, observability, security, failure recovery, capacity management, and reproducible upgrades.