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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
Write a SQL query to build a cohort retention table: for each user cohort (signup_week) compute percentage of users active in weeks 0..12 after signup. Use window functions and CTEs to structure the pipeline. Explain how you prevent double-counting and handle users with missing activity weeks.
MediumTechnical
0 practiced
Provide a scenario where a recursive CTE combined with window functions is useful in BI analytics. Then write a sample SQL (any dialect that supports recursion) that uses a recursive CTE to expand weekly snapshots into cumulative weekly metrics for a retention-like calculation.
MediumTechnical
0 practiced
Explain how frame exclusion works in window functions. Provide SQL that demonstrates using EXCLUDE CURRENT ROW (or equivalent) and show how it differs from ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING. Give an example where exclusion yields different results due to peer rows.
EasyTechnical
0 practiced
Discuss FIRST_VALUE, LAST_VALUE and NTH_VALUE. Explain why LAST_VALUE can return unexpected results without proper framing, and demonstrate with SQL how to get the true last non-null value per partition using an appropriate frame or alternative functions.
HardTechnical
0 practiced
Describe how NULL ordering (NULLS FIRST / NULLS LAST) and IGNORE NULLS semantics influence FIRST_VALUE and LAST_VALUE. Demonstrate SQL examples in a dialect with IGNORE NULLS and one without, showing how to get the first non-null value per partition.

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