Handling Problem Variations and Constraints Questions

This topic covers the ability to adapt an initial solution when interviewers introduce follow up questions, new constraints, alternative optimization goals, or larger input sizes. Candidates should quickly clarify the changed requirement, analyze how it affects correctness and complexity, and propose concrete modifications such as changing algorithms, selecting different data structures, adding caching, introducing parallelism, or using approximation and heuristics. They should articulate trade offs between time complexity, space usage, simplicity, and robustness, discuss edge case handling and testing strategies for the modified solution, and describe incremental steps and fallbacks if the primary approach becomes infeasible. Interviewers use this to assess adaptability, problem solving under evolving requirements, and clear explanation of design decisions.

EasyTechnical

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Explain the practical differences between batch and stream processing for a data pipeline. Given a batch job that processes hourly files, describe three scenarios where you would convert it to streaming and what concrete changes you'd make to the architecture and correctness handling.

MediumSystem Design

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Design a Kafka-based ingestion system that must accept bursts up to 10k events/sec while guaranteeing per-user rate limits (100 req/min). Describe how you would enforce rate limits, handle backpressure to producers, partitioning strategy, and the consumer topology to process the data safely.

HardTechnical

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You need to compute a join between two very large datasets where neither side fits in memory. Describe at least three scalable join algorithms you might use (e.g., repartitioned hash join, sort-merge join, block nested loop with bloom filters) and explain when each is preferable and what constraints would force you to choose an approximate approach instead.

MediumTechnical

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Your distributed join is suffering from data skew: a handful of keys are orders of magnitude larger than others causing stragglers. List detection techniques and propose at least three concrete mitigation strategies (e.g., salting, broadcasting, sampling-based repartition) with trade-offs for each.

MediumTechnical

0 practiced

When distinct counts are required for millions of users and memory per worker is limited, which approximate algorithm would you choose and why? Discuss HyperLogLog: memory vs accuracy trade-offs, mergeability across partitions, and how to test error bounds in production.

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