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.

EasyTechnical

34 practiced

A Flask prediction endpoint currently loads the model from disk on every request, causing high latency. Without major infra changes, what immediate code and packaging changes would you make to reduce latency and make the codebase more maintainable? Mention process-level and code-level changes and how you'd validate the improvements.

EasySystem Design

42 practiced

Explain what an API contract for a model inference service should include. Provide a compact JSON-like example of an input schema, output schema, error codes and a versioning header that clients must send. Discuss how such contracts help component boundaries and backward compatibility.

EasyTechnical

40 practiced

You inherit a single Python file where preprocessing, feature engineering, training, and a Flask-based prediction endpoint are mixed together. Identify coupling and cohesion issues and propose a refactor plan (modules and boundaries) to improve maintainability and scalability. Explain what tests you would add before and after refactor and how to migrate with minimal production risk.

MediumTechnical

30 practiced

A read-heavy feature store is contributing to inference latency. At the application layer (not DB internals) propose strategies to reduce latency: local caches, tiered caches, denormalized feature bundles, precomputed aggregates, TTL strategies, and async fetch patterns. Discuss consistency and invalidation implications for each strategy.

MediumTechnical

38 practiced

What patterns and checks should you use to move experiment code (Jupyter notebooks) into production-ready artifacts? Suggest templates, automated checks, code review requirements, feature flags, and a lightweight staging environment to validate experiments before production push.

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