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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
You need the average of the last 5 distinct event types per user (by most recent occurrence). Propose an SQL approach using window functions or CTEs to select the last 5 distinct event types per user and compute the average of an associated metric for those events.
EasyTechnical
0 practiced
Explain the purpose and components of the SQL OVER clause when used with window functions. Describe how PARTITION BY and ORDER BY inside OVER change the result set, and provide a compact example using ROW_NUMBER() over partitions of country ordered by revenue to illustrate the differences.
MediumTechnical
0 practiced
Implement a 7-day moving average of daily revenue per product using window functions. Explain the difference between using ROWS BETWEEN 6 PRECEDING AND CURRENT ROW and RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW, especially when dates are irregular or have gaps.
HardTechnical
0 practiced
Explain why LAST_VALUE(value) OVER (ORDER BY ts) can return a value from following rows by default and produce incorrect 'last seen up to now' semantics. Provide the corrected frame clause to ensure LAST_VALUE returns the latest value up to the current row only.
HardTechnical
0 practiced
A slow analytics query computes several aggregates by joining the same aggregated subquery multiple times. Show how to rewrite the query using window functions to compute all aggregates in one pass, provide an example, and discuss trade-offs in readability and optimizer behavior.

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