Complex Data Integration and Joins Questions

Handling intricate join scenarios: multi-condition joins, conditional joins with complex logic, joining on date ranges or overlapping time periods, complex left joins with multiple filtering conditions, self-joins for hierarchical or relationship data, handling non-standard relationships between tables. Understanding implications of different join types on row counts, NULL values, and duplicate handling. Designing queries that correctly integrate data from multiple sources while maintaining data integrity and avoiding duplicate counting or missing data.

MediumTechnical

43 practiced

Write a SQL query to deduplicate a sales table Sales(sale_id INT, order_id INT, product_id INT, amount DECIMAL, created_at TIMESTAMP) where duplicates are defined as same order_id and product_id within 5 minutes. Keep the latest created_at per duplicate group and mark others as duplicates. Provide an approach that works in ANSI SQL and explain how it avoids accidental data loss.

MediumTechnical

35 practiced

A dashboard metric (daily active users) is derived from joining a raw Events table to a Users table. The Users table contains multiple rows per user for auditing. Explain several strategies to deduplicate Users before joining (last-effective record, effective_date, using surrogate keys), write example SQL for one approach, and discuss downstream impacts.

EasyTechnical

46 practiced

Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN in the context of reporting metrics. Provide examples of when a BI dashboard might mistakenly use the wrong join type and produce overcounting or missing rows.

MediumTechnical

36 practiced

Create a SQL pattern to join and roll up multi-currency transaction tables into a daily revenue table while ensuring that exchange rates used are the rates valid on transaction_date. Include sample join predicates and discuss how to handle missing rates for weekends or holidays.

MediumTechnical

35 practiced

Provide SQL to compute an adjusted conversion rate where conversions are stored across two tables: ConversionsA(user_id, conv_ts) and ConversionsB(user_id, conv_ts). Some conversions appear in both tables (duplicates). Join both sources to Users and calculate unique conversions per user per month. Explain how you prevent double counting across the two sources.

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