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.

EasyTechnical

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In Apache Spark, explain at a high level what broadcast (map-side) joins and shuffle (sort-merge or shuffle-hash) joins are. Describe the performance trade-offs, memory and network implications, and rules of thumb for choosing to broadcast a table versus allowing a full shuffle.

EasyTechnical

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What is partitioning on storage and within processing frameworks, and why is it important for scaling large analytical datasets? Explain how partitioning affects query pruning, data locality, parallelism, file sizes, and metadata overhead, and give practical guidance for choosing partition keys.

HardSystem Design

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Design a petabyte-scale data lake optimized for fast analytical queries while minimizing ongoing storage and compute costs. Specify file format choices, partitioning strategy, cataloging solution (e.g., Hive metastore or Glue), compaction and small-file mitigation, caching layers, access-control, and cost-control mechanisms. Discuss trade-offs between ingest speed and query latency and propose monitoring/governance practices to maintain performance over time.

EasyTechnical

0 practiced

A nightly ETL job currently runs on a single high-memory instance to avoid disk spills and out-of-memory errors. Explain how you would evaluate the trade-offs between paying for larger memory instances, redesigning the job to be disk-backed (chunked/external), or migrating to distributed processing. What metrics and cost model would you use to decide?

MediumTechnical

0 practiced

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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