CAP Theorem and Consistency Models Questions

Understand the CAP theorem and how Consistency, Availability, and Partition Tolerance interact in distributed systems. Know different consistency models including strong consistency such as linearizability, eventual consistency, causal consistency, and session consistency, and how to apply them to different use cases. Be familiar with consensus protocols and distributed coordination primitives such as Raft and Paxos, quorum reads and writes, two phase commit and when to use them. Understand trade offs between consistency and availability under network partitions, patterns for hybrid approaches where different data uses different guarantees, and the product and developer experience implications such as latency, stale reads, and API contract clarity.

EasyTechnical

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Explain causal consistency and session consistency and illustrate each with a short example involving personalization features used by ML models. How can causal or session guarantees improve user experience in a recommendation flow compared to eventual consistency?

HardTechnical

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Design a consistency SLA and operational monitoring plan for ML model APIs. Define SLOs for stale reads (acceptable staleness window), time-to-consistent after write, error rates during partitions, and automated remediation actions. Explain how these SLAs influence implementation and runbook design.

HardTechnical

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Design a streaming ingestion pipeline for feature computations with exactly-once semantics for downstream feature stores used in training. Discuss checkpointing, idempotent writes, transactional sinks, integration options with systems like Kafka and Flink, and recovery behavior after failures to preserve exactly-once guarantees.

EasyTechnical

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What is eventual consistency and how can stale reads affect ML predictions in production? Provide two concrete examples where eventual consistency is acceptable for ML outputs and two where it would lead to serious correctness or safety issues.

HardTechnical

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Design an automated system to detect and compensate for training-serving skew caused by eventual consistency of feature updates. Specify metrics to compute skew, alerting thresholds, automated rollback triggers, and a remediation pipeline that can re-train or re-serve models as needed.

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