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.

HardTechnical

0 practiced

Implement a simplified two-phase commit (2PC) coordinator and participant simulation in Python. Provide Coordinator.prepare(transaction_id, participants), Coordinator.commit(transaction_id) and Participant.respond_prepare(transaction_id) functions. Simulate message loss and participant failure in tests and describe 2PC failure modes and blocking behavior.

HardSystem Design

0 practiced

Design a distributed configuration service for model hyperparameters, feature flags, and rollout gates that provides strong consistency for critical operations and low read latency for inference services. Choose a consensus algorithm, caching/invalidation strategy, and explain read/write flows and how failures are handled.

MediumTechnical

0 practiced

Design a canary deployment plan for a new model version across a distributed serving cluster. Include traffic splitting strategy, monitoring metrics (latency, error rate, model-quality metrics), statistical tests to decide promotion, rollback triggers, and how you would handle delayed signals such as conversions.

HardTechnical

0 practiced

Your distributed inference pipeline experiences cascading failures: a downstream feature-service becomes slow and causes upstream request buildup and timeouts. Describe how to apply backpressure, design timeouts and retry logic, use bulkheads and circuit breakers, and architect graceful degradation paths (e.g., fallback to cached or simpler models).

HardSystem Design

0 practiced

Design a scalable metadata service for models (model registry storing artifact locations, lineage, hyperparameters) that supports hundreds of concurrent writes and reads with strong consistency guarantees for critical operations (e.g., model promotion). Explain partitioning, consensus choice, caching, and how to handle hot-spot workloads.

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