Performance Engineering and Cost Optimization Questions

Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.

MediumTechnical

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Discuss how row-level security (RLS) policies, user-defined functions (UDFs), and complex JSON parsing in queries can affect performance and cost. Propose detection strategies (logs, explain plans) and mitigation tactics to protect warehouse performance.

MediumTechnical

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Explain batching, backpressure, and asynchronous processing in the context of BI ingestion pipelines (e.g., Kafka -> Spark/Flink). Provide an example design that increases throughput without sacrificing maximum allowable data freshness for dashboards.

EasyTechnical

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How do approximation algorithms (HyperLogLog, Count-Min Sketch) trade accuracy for performance and cost in BI metrics? Give an example where approximate counts are acceptable and another where exact counts are required. Explain how to communicate these trade-offs to stakeholders.

EasyTechnical

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Explain how compression codecs and column encodings reduce storage and IO for analytics. Given numeric (transaction_amount) and high-cardinality string (user_id_hash) columns, recommend encoding/compression techniques and explain why.

HardTechnical

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You migrated your ETL from a small vendor scheduler to a new framework and noticed dashboard load times tripled. Design an end-to-end profiling plan to narrow down the root cause: include data validation steps, query comparisons, ETL staging checks, and an A/B test approach to confirm the cause.

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