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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
18 practiced
Your team uses BigQuery and charges are dominated by bytes scanned. Propose query refactor strategies and table design choices to reduce query costs for repeated ML experiments: include partitioning, clustering, materialized views, column selection, and data format choices. Provide small SQL examples that illustrate cost-saving techniques.
HardSystem Design
30 practiced
A training query runs slowly because it repeatedly joins several wide denormalized tables. Design a materialized view strategy to precompute the heavy joins and describe an incremental refresh approach using change tracking tables or partitioned refresh, including SQL patterns for performing atomic swap of refreshed partitions.
MediumTechnical
20 practiced
Write a SQL query to compute a running total of 'amount' per user ordered by event_time using window functions in PostgreSQL. Make sure the running total resets per user and mention how you would handle identical timestamps for ordering deterministically.
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.
MediumTechnical
20 practiced
Your daily ETL joins event data to user profiles to compute features. Occasionally events arrive late (up to 48 hours). Describe strategies to handle late-arriving data in SQL-based pipelines, including incremental backfills, watermarking, and partitioned reprocessing of only affected partitions. Provide sample SQL patterns to reprocess recent partitions.

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