Distributed Systems Fundamentals Questions

Core principles and theory that underlie distributed computing systems. Includes understanding trade offs between consistency, availability, and partition tolerance, common consistency models such as eventual and strong consistency, replication and sharding strategies, load balancing and data partitioning, consensus algorithms and their guarantees, scalability and fault tolerance patterns, and how these concepts apply to infrastructure components such as databases, caches, service meshes, and load balancers. Candidates are expected to explain design choices, common failure modes, and how fundamental concepts influence architecture decisions for resilient and scalable systems.

MediumTechnical

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Explain caching strategies (LRU, TTL, write-through, write-back) for an online feature cache used by inference. For features updated frequently but read far more often, recommend a strategy and discuss invalidation and cache warming approaches.

MediumTechnical

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Design and implement (pseudocode or Python) a retry wrapper for inference requests that uses exponential backoff with jitter. Requirements: idempotent requests only, configurable max retries, base/backoff cap, and support for per-request timeout. Explain how this design avoids thundering herd in failures.

EasyTechnical

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What is backpressure in distributed systems and why is it important in ML training and inference pipelines? Describe how message brokers (e.g., Kafka) or client-side throttling can provide backpressure and what happens if you ignore it.

MediumSystem Design

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Design an architecture that supports both online (real-time) feature computation and offline precomputed features for training. Explain how you ensure feature parity between online and offline stores, manage freshness, and handle fallback when online compute fails.

HardTechnical

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Propose an incident response runbook for P99 latency regressions in an ML inference system spanning multiple microservices. Include immediate mitigation steps, triage checks, communication plan, rollback criteria, and post-incident analysis actions.

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