Your question is Fault-Tolerant Training Cluster Execution Plan. 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).
You’ve been asked to lead the execution plan for a fault-tolerant distributed training cluster that will support large model training runs. The current environment can complete small jobs, but long-running multi-node training is vulnerable to node loss, network instability, storage bottlenecks, and slow recovery after failures. The project matters because repeated training interruptions are delaying model delivery and driving up compute waste, but different stakeholders want different outcomes: researchers want maximum throughput, infrastructure wants reliability, and finance wants tighter control of GPU spend.
How would you architect and execute a fault-tolerant system for a distributed training cluster?