Your question is Fault-Tolerant Cloud-Native Systems. 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 fault-tolerant, highly available distributed systems for cloud-native infrastructure?
Discuss the design principles, architecture, and operational practices you would use for an ML platform that supports distributed data processing, model training, multi-stage inference, and online serving. Clarify requirements before committing to scale assumptions.
Address service decomposition, failure isolation, replication, state management, deployment strategy, observability, disaster recovery, and graceful degradation. Explain how you would validate availability and handle training-serving skew, feature drift, dependency failures, and regional outages.
