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Advanced SQL Window Functions Questions

Mastery of Structured Query Language window functions and advanced aggregation techniques for analytical queries. Core function families include ranking functions such as ROW_NUMBER, RANK, DENSE_RANK, and NTILE; offset functions such as LAG and LEAD; value functions such as FIRST_VALUE, LAST_VALUE, and NTH_VALUE; and aggregate window expressions such as SUM OVER and AVG OVER. Candidates should understand the OVER clause with PARTITION BY and ORDER BY, frame specifications using ROWS BETWEEN and RANGE BETWEEN, tie handling, null behavior, and how frame definitions affect results. Common application patterns include top N per group, deduplication using row numbering, running totals and cumulative aggregates, moving averages, percent rank and distribution calculations, event sequencing and period over period comparisons, gap and island analysis, cohort and retention analysis, and trend and growth calculations. The topic also covers structuring complex queries with Common Table Expressions including recursive Common Table Expressions to break multi step analytical pipelines and to handle hierarchical or iterative problems, and choosing between window functions, GROUP BY, joins, and subqueries for correctness and readability. Performance and correctness considerations are essential, including join and sort costs, index usage, memory and sort spill behavior, execution planning and query optimization techniques, and trade offs across different database dialects and large data volumes. Interview assessments typically ask candidates to write and explain queries that use these functions, reason about frame semantics for edge cases such as ties, nulls, and partition boundaries, and to rewrite or optimize expensive queries.

MediumTechnical
0 practiced
Design a 4-week cohort retention query using window functions and CTEs. Given users(user_id, signup_date) and events(user_id, event_date), produce a table where each signup week (cohort) has columns for week0 (signup week), week1, week2, week3 retention percentages. Show the SQL approach and explain how window functions simplify the computation of per-user week offsets.
EasyTechnical
0 practiced
Explain the difference between PERCENT_RANK() and CUME_DIST(). Include formulas or definitions, and give an example scenario where one is preferable to the other for analyst reporting.
EasyTechnical
0 practiced
Discuss default frame behavior for window aggregate functions when ORDER BY is present and when it is not. Explain why LAST_VALUE can be surprising and show how to define a frame explicitly so LAST_VALUE returns the last value in a partition regardless of the current row position.
HardTechnical
0 practiced
An ETL query that joins a large user_events table to user_profiles and then computes multiple window functions is spilling to disk during sorts. The explain plan shows large shuffle/sort operations. Given the SQL, explain step-by-step how you would optimize it: consider join ordering, pushing filters, using pre-aggregated data, partition/prune, and materializing intermediate results. Provide a prioritized action list.
MediumTechnical
0 practiced
Given purchases(user_id, purchase_date, purchase_amount), write a query to return each user's 3rd purchase date using NTH_VALUE and also provide an alternative implementation using ROW_NUMBER(). Explain trade-offs and portability between the two approaches across different SQL dialects.

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