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.

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Design observability for a model-serving platform: list concrete SLIs and SLOs you would track (latency percentiles, error rates, model quality metrics, input distribution drift, and feature coverage). Explain alert thresholds, dashboards, and automated mitigations to reduce toil and MTTR.

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Design a chaos-engineering experiment for the inference microservice that validates resilience against partial network partitions and increased latency. Define the hypothesis to test, blast-radius limits, success/failure metrics, steps to run the experiment, and rollback criteria.

EasyTechnical

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Compare active-active and active-passive failover strategies for serving production ML models across availability zones. For each approach explain failure detection, state synchronization, write consistency, and expected recovery latency when an AZ fails.

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.

MediumTechnical

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Write pseudocode (Python-like) that implements backpressure for a streaming inference pipeline: producers push events to a bounded queue, a fixed worker pool consumes events, and the system either pauses producers, returns 429 to the source, or persists to a durable queue when high-water marks are reached. Include high and low water marks and how producers are notified.

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