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Advanced Querying with Structured Query Language Questions

Covers authoring correct, maintainable, and high quality Structured Query Language statements for analytical and transactional problems. Candidates should demonstrate writing Select Insert Update and Delete statements and using filtering grouping ordering and aggregation correctly. Emphasis is on complex query constructs and patterns such as multi table joins and join condition logic self joins for hierarchical data nested and correlated subqueries common table expressions including recursive common table expressions window functions such as row number rank dense rank lag and lead set operations like union and union all and techniques for calculating running totals moving averages cohort metrics and consecutive event detection. Candidates should be able to break down and refactor complex requirements into composable queries for readability and maintainability while reasoning about performance implications on large data sets. Senior expectations may include mentoring on best practices for query composition and understanding how schema and configuration choices influence query performance.

HardTechnical
21 practiced
A critical join for your model training is slow on a 300M row table. Walk through the steps you'd take using EXPLAIN ANALYZE in PostgreSQL to identify the bottleneck and optimize it. Mention specific plan indicators to look for (seq scan, nested loop, hash build, actual vs estimated rows) and concrete fixes (indexes, stats, join order, temp tables).
HardTechnical
22 practiced
For very high-cardinality distinct counts in SQL, explain approximate distinct algorithms available in modern warehouses (e.g., HyperLogLog via approx_count_distinct, HLL sketches). Show an example SQL in BigQuery or Postgres extension and discuss the accuracy vs memory trade-offs and merging sketches across partitions.
MediumTechnical
19 practiced
Discuss the differences between common table expressions (CTEs) and temporary tables in terms of readability, optimization, and performance in Postgres and other databases. When might a temporary table be preferable over a CTE for large intermediate results?
HardTechnical
22 practiced
Write an anti-join SQL pattern that removes users who appear in a fraud_events table from a users table, focusing on performance at scale. Compare and discuss NOT IN, NOT EXISTS, and LEFT JOIN ... IS NULL semantics and pitfalls related to NULL values and indexing.
HardTechnical
19 practiced
Show how to detect cardinality estimation errors in PostgreSQL using EXPLAIN ANALYZE by comparing estimated rows to actual rows. Explain how creating multi-column statistics or increasing statistics target can help, and provide SQL commands to create extended statistics.

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