Fault Tolerance and System Resilience Questions

Designing systems to anticipate, tolerate, contain, and recover from component and network failures while minimizing customer impact and preserving correctness. Topics include identifying common failure modes and single points of failure, redundancy and isolation patterns at hardware, service, and geographic levels, and failover strategies including active active and active passive. Cover retry policies with exponential backoff, timeouts, circuit breaker and bulkhead patterns, graceful degradation, rate limiting, and backpressure techniques to protect systems during overload. Discuss orchestration of node rejoin and state rebuild, replication strategies and consistency trade offs, leader election and consensus implications, and techniques to avoid and mitigate split brain. Explain monitoring, health checks, alerting, and metrics such as mean time to recovery and mean time between failures to guide operational improvements. Include testing for resilience through chaos engineering and fault injection, handling flaky components in test environments, analysis of past failures and refactoring for resiliency, and operational practices that reduce blast radius and speed recovery.

MediumTechnical

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Explain strategies to avoid split-brain in leader-based replication systems for model metadata, including fencing tokens, lease-based leaders, and quorum-based leader election. For each strategy describe trade-offs and operational considerations.

EasyTechnical

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Explain the difference between fault tolerance and system resilience specifically in the context of AI systems (both model training and inference). Give concrete examples (e.g., redundant GPU nodes, checkpointing, graceful degradation when a feature store is unavailable), and explain why both properties are important for production ML workloads.

HardSystem Design

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Architect a globally distributed serving platform for a large language model (tens of GBs) that must handle 1M requests/min with 200ms P95 latency across three geographic regions. Cover model sharding/replication, GPU autoscaling, inference caching, cold-start strategies, multi-region failover, personalization data consistency, and privacy constraints.

HardTechnical

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Describe techniques to detect silent failures where a model server returns plausible but incorrect predictions due to corrupted weights, bitrot, or subtle hardware faults. Propose automated safety checks, canary/verifier pipelines, shadow traffic strategies, and methods to quarantine and roll back affected nodes.

EasyTechnical

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What is the bulkhead pattern and how would you apply it at different levels (GPU pools, connection pools, tenant isolation) in a multi-tenant AI inference platform to limit blast radius from noisy tenants or failing components?

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