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

38 practiced

Design an SLA-aware router for a fleet of data processing jobs with heterogeneous SLAs. The router must prioritize high-SLA jobs, perform load-shedding under overload, and avoid starvation of lower-priority tasks. Explain scheduling algorithms (priority queues, weighted fair queuing, aging) and trade-offs.

HardSystem Design

45 practiced

Evaluate pre-aggregation (materialized cubes / OLAP) versus on-demand ad-hoc query engines for interactive analytics. Propose a hybrid architecture that balances freshness, storage cost, query latency, and maintenance complexity for a BI platform used by analysts and dashboards.

HardTechnical

33 practiced

Design a globally unique ID generation solution for a multi-region platform that minimizes coordination on the critical path. Compare approaches such as Twitter Snowflake, UUIDv1, Hi-Lo, and time-sharded sequences, and discuss clock skew, throughput, collision risk, and monotonicity concerns.

HardTechnical

26 practiced

Implement in pseudocode a Kafka custom partitioner that balances ordering per user while preventing hotspots from very high-traffic users. Explain how you'd detect hotspots and mitigate them (e.g., special partitions, sharding heavy keys) while minimizing rebalancing impact.

HardSystem Design

32 practiced

Architect an anti-entropy and reconciliation mechanism for eventual-consistent data replicated across distributed services where out-of-order and duplicate events can occur. Include detection, reconciliation algorithms (digests, Merkle trees), deterministic conflict resolution policies, and operational tooling for large datasets.

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