Optimization and Technical Trade Offs Questions

Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.

HardTechnical

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When and how would you use approximate algorithms (HyperLogLog, Count-Min Sketch, Bloom filters) in a production data pipeline? For each algorithm, explain error characteristics, memory footprint, whether they are mergeable, and practical use-cases (cardinality estimates, heavy-hitter detection, membership checks). Give examples of business metrics where approximation is acceptable and how to validate error bounds.

HardSystem Design

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Design a disaster recovery plan for critical data pipelines with an RPO (Recovery Point Objective) of 5 minutes and an RTO (Recovery Time Objective) of 30 minutes. Describe backup strategies, checkpointing frequency, cross-region replication, warm standbys vs cold failover, testing drills, and cost trade-offs.

EasyTechnical

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Explain eventual consistency and strong consistency in the context of a data ingestion pipeline that writes to multiple storage layers (message queue, data lake, OLAP store). Give concrete failure scenarios (duplicate writes, missing updates, delayed visibility) and discuss how each consistency model affects downstream analytics and SLA guarantees.

MediumTechnical

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A long-running Spark job shows intermittent 10-20 second GC pauses causing missed SLAs. Describe how you would diagnose GC problems and list concrete tuning and code-level strategies to reduce GC impact, such as changing GC algorithm, resizing executor heaps, reducing object churn, using off-heap storage, kryo serialization, and memory fraction tuning.

MediumTechnical

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For a streaming aggregation job that must handle out-of-order events, explain how you would choose watermarking and checkpointing intervals to balance correctness (complete results), latency, and state size. Discuss trade-offs of setting watermarks too early or too late and the cost/benefit of frequent checkpointing.

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