System Design in Coding Questions

Assess the ability to apply system design thinking while solving coding problems. Candidates should demonstrate how implementation level choices relate to overall architecture and production concerns. This includes designing lightweight data pipelines or data models as part of a coding solution, reasoning about algorithmic complexity, throughput, and memory use at scale, and explaining trade offs between different algorithms and data structures. Candidates should discuss bottlenecks and pragmatic mitigations such as caching strategies, database selection and schema design, indexing, partitioning, and asynchronous processing, and explain how components integrate into larger systems. They should be able to describe how they would implement parts of a design, justify code level trade offs, and consider deployment, monitoring, and reliability implications. Demonstrating this mindset shows the candidate is thinking beyond a single function and can balance correctness, performance, maintainability, and operational considerations.

HardSystem Design

0 practiced

Design a global multi-region model serving architecture to achieve average end-to-end inference latency under 50ms for users worldwide. Consider model versioning, feature freshness, replication, caching strategy, DNS or edge routing, and how to minimize staleness for features while tolerating network partitions.

EasyTechnical

0 practiced

Write minimal Flask-like pseudocode for a /health endpoint for a model server that verifies: the model is loaded, memory usage below a configurable threshold, and last successful inference timestamp within the last N seconds. Include appropriate HTTP codes and a brief explanation of how this endpoint should be used by orchestration systems.

HardTechnical

0 practiced

Design an end-to-end A/B/Continuous evaluation system that automatically monitors multiple models in production and promotes the one with the best long-term business metric. Discuss statistical pitfalls (peeking, multiple comparisons), how to incorporate delayed rewards, safe promotion/rollback, and governance controls for automated promotions.

MediumSystem Design

0 practiced

Design a distributed cache for model artifacts to speed up model loading across 1,000 nodes with 10,000 model loads/day. Constraints: limited network bandwidth, cache hit latency under 20ms, and eviction under memory limits. Discuss metadata, consistency, lease/TLS considerations, and how to invalidate or rollout new model versions.

HardTechnical

0 practiced

Provide a scalable approach (algorithm design or code sketch) to compute per-user rolling metrics—such as CTR over the past 24 hours—used by online features, given high write throughput and eventual consistency constraints. Discuss storage structures, how to update metrics efficiently on each event, sampling, and how to handle late-arriving events.

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