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

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Provide a checklist and concrete examples for writing unit and integration tests for BI code that calculates metrics and renders visualizations. Cover: isolating data sources, mocking time, deterministic randomness, testing caching behavior, and validating output formats (CSV, JSON, chart data).

HardSystem Design

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A dashboard that uses server-sent events (SSE) shows live metrics. Describe how you would design backpressure and resource controls so a burst of subscribers doesn't overwhelm metric processors. Include connection limits, sampling, and graceful degradation approaches.

MediumTechnical

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You're asked to evaluate the impact of adding optimistic locking for a shared in-memory cache used by BI services to avoid racey recomputations. Explain how optimistic locking would work here, what failures you must handle, and compare this approach to using a central coordination store (e.g., Redis locks).

EasyTechnical

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As a BI lead you must convince engineering leadership to invest in a shared component library for dashboard widgets. Prepare a one-paragraph technical argument that summarizes ROI, risk reduction, onboarding benefits, and how you would measure success after 6 months.

EasyTechnical

0 practiced

Describe the trade-offs between keeping a single monolithic BI service versus decomposing into microservices (e.g., ingestion, metrics calculation, presentation). Focus on developer productivity, deployment frequency, operational complexity, and how it affects dashboard evolution in a medium-size company (2000 employees).

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