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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Design an A/B testing framework for two model versions in production: explain user assignment, ensuring deterministic bucketing, measuring metrics, calculating statistical significance, and automated promotion/rollback criteria. Include thoughts on experiment leakage and duration.

HardTechnical

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Explain how CRDTs (Conflict-free Replicated Data Types) or vector clocks can be used to reconcile conflicting feature updates across regions in an eventually-consistent feature store. Provide an example feature type where a CRDT is appropriate and one where it's not.

MediumTechnical

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Compare parameter server and AllReduce approaches for gradient synchronization in distributed training. For a sparse, high-cardinality recommendation model, which approach is preferable and why? Discuss network bandwidth and staleness implications.

HardTechnical

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Design a streaming pipeline (using tools like Kafka Streams or Flink) to compute online features with exactly-once semantics and low latency. Explain state management, checkpointing, windowing for aggregations, and how to expose these features to low-latency inference endpoints.

HardTechnical

0 practiced

For extremely large models that require model parallelism across multiple GPUs/machines, propose an efficient strategy combining pipeline parallelism, tensor-slicing, and gradient compression. Discuss scheduling, memory balancing, and the effect on throughput and latency.

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