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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.

HardSystem Design
0 practiced
Design an SQL-based streaming feature aggregation pipeline that aims for exactly-once semantics when ingesting event streams into a feature table. Explain how you would use transactional upserts, idempotent keys, and offset tracking to achieve correctness, and outline failure recovery steps when a connector fails mid-batch.
HardTechnical
0 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).
HardSystem Design
0 practiced
Explain the importance of data locality and colocation for repeated joins in distributed query engines. Provide concrete partitioning/bucketing strategies that minimize shuffle for common join patterns in ML pipelines and examples of how to implement them in SQL write paths.
HardSystem Design
0 practiced
Your ML feature tables include wide sparse feature sets and many small lookup tables. Propose schema and SQL query strategies (denormalization, wide tables, JSON columns, columnar file formats) to balance query performance for offline training and storage cost. Explain trade-offs in terms of read amplification and write complexity.
MediumTechnical
0 practiced
Write SQL that computes a 7-day moving average of a daily metric using window functions and handles missing days by filling gaps. Assume a table daily_metrics (day date, metric numeric). Provide a solution that works in PostgreSQL and describe how you ensure missing days are included.

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