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.

HardTechnical

0 practiced

Design a storage tiering policy (hot/warm/cold) for historical analytics data spanning 10 years to balance cost and query latency. Provide rules for data movement (age thresholds, access frequency), expected cost savings, and the impact on ad-hoc analyst queries.

MediumTechnical

0 practiced

Implement a rolling 7-day active users calculation in SQL (Postgres or Snowflake) that returns daily counts of unique users who were active in the 7-day window ending on each date. Provide performant SQL using window functions and explain how partitioning or materialized views can make this efficient for large datasets.

EasyTechnical

0 practiced

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.

MediumTechnical

0 practiced

Given a large orders table with schema: orders(order_id, customer_id, order_ts, total_amount, status), and customers(customer_id, country), a slow query joins and aggregates over a year of data. Show how to rewrite and optimize the query for performance (add indexes, push filters, use partitions). Provide the rewritten SQL and describe why it performs better.

MediumTechnical

0 practiced

A critical dashboard query involves a heavy group-by on a high-cardinality column. Propose approaches to accelerate this query: approximate top-k, pre-aggregations, bloom filters, or sharded aggregation. For each, explain complexity, accuracy impacts, and expected resource savings.

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