Scalability and Code Organization Questions

Focuses on designing software and codebases that remain maintainable and performant as features and user load grow. Areas include modularity and separation of concerns, component and API boundaries, when and how to refactor, trade offs between monolith and service oriented architectures, data partitioning and caching strategies, performance optimization, testing strategies, dependency management, code review practices, and patterns for maintainability and evolvability. Interview questions may ask candidates to reason about design choices, identify coupling and cohesion issues, and propose practical steps to evolve an existing codebase safely.

MediumTechnical

0 practiced

You need to split a monolithic repo that contains training, serving, and experiment notebooks into separate services. Given a 4-person team and a 6-week timeline, outline a prioritized migration plan that minimizes production risk. Include steps for API contracts, tests, rollout, and rollback strategies.

HardSystem Design

0 practiced

Design a distributed feature caching layer for per-user personalization that serves 100M users. Focus on application-level partitioning, cache hierarchy (edge, region, central), invalidation on upstream updates, hot-key mitigation, and the consistency model you choose. Explain how you would route requests and where to perform cache lookups.

HardTechnical

0 practiced

CPU-bound feature computations are the inference bottleneck. Propose a prioritized plan to optimize: consider precomputing features, approximate/quantized features, rewriting hot code with vectorized kernels or JIT (numba), and moving heavy transforms to specialized services. Discuss trade-offs of accuracy, staleness, and engineering cost.

HardTechnical

0 practiced

You have two implementations of feature transformations: an offline library used in training and a separate online library used in inference. Propose a code organization and build/packaging approach to ensure parity, minimize duplication, and allow hotfixes to propagate safely into production. Include testing patterns to validate parity across versions.

EasyTechnical

0 practiced

Describe three caching strategies applicable to model-serving: prediction result caching, feature value caching, and model artifact caching. For each strategy explain a typical cache key design, expected hit-rate influences, freshness concerns, and how staleness can affect model quality or business metrics.

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