Architecture and Technical Trade Offs Questions

Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.

HardTechnical

31 practiced

In Python, implement a function reconcile_events(events, window_start, window_end) that aggregates event counts per 'key' within a time window while deduplicating events using 'event_id' and handling out-of-order timestamps. Sample input:\n\nevents = [\n {'event_id':'a1','key':'page','ts':100,'count':1},\n {'event_id':'a2','key':'page','ts':95,'count':1},\n {'event_id':'a1','key':'page','ts':100,'count':1} # duplicate\n]\n\nExpected output for window 90-110: {'page': 2}\n\nImplement efficient logic and explain how it bounds memory usage for very large streams.

HardTechnical

35 practiced

Develop a quantitative model to estimate the change in infrastructure cost and query latency when reducing dashboard refresh interval from 60 minutes to 1 minute across a fleet of 200 dashboards. Describe assumptions, variables (compute per query, concurrency, cache reuse), and how you'd validate the model with a pilot. Provide example calculations or formulas you would use.

EasyTechnical

29 practiced

Define Service Level Agreements (SLA) and Service Level Objectives (SLO) and explain how you'd apply them to a BI reporting system. Provide concrete examples such as an SLO for dashboard load time (e.g., p95 < 2s), an SLO for data freshness, and an SLA-level commitment to a business unit. Describe how you'd monitor and enforce these promises.

HardTechnical

32 practiced

Compare caching at CDN/edge locations versus application-level caches for serving large-scale dashboards to global users. Discuss trade-offs in consistency, invalidation complexity, cost, geographic latency, and suitability for dynamic user-specific content. Provide example scenarios where edge caching is appropriate and where it is not.

MediumTechnical

28 practiced

Discuss the trade-offs of pre-aggregating metrics (materialized views or rollups) versus computing aggregates on demand for dashboards. Consider freshness, storage cost, query latency, variety of ad-hoc queries, and operational complexity. Provide concrete scenarios where pre-aggregation is clearly preferable and where on-demand compute is better.

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