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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
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
Explain the difference between ROWS and RANGE frame specifications in the OVER clause. Provide an example dataset where ROWS BETWEEN 1 PRECEDING AND CURRENT ROW gives different results from RANGE BETWEEN INTERVAL '1 day' PRECEDING AND CURRENT ROW due to duplicate timestamps or value ties.
MediumTechnical
0 practiced
Given page_views(session_id, view_ts timestamp, page_url), write SQL to assign session_event_number (1,2,3...) per session and compute time_diff_seconds from previous event. Filter out sessions with only one event. Use window functions in your solution and mention how you handle ordering ties.
HardTechnical
0 practiced
You have a complex query with multiple window functions, each with its own partition and order. Rewrite the query into a series of CTEs (pipelined stages) to improve readability and maintainability. Provide both the original single-query version and the refactored CTE pipeline, and discuss potential performance trade-offs.
EasyTechnical
0 practiced
Explain difference between percent_rank() and cume_dist(). Provide a small example dataset and show the numeric outputs for both functions. Describe which is more appropriate for percentile-based dashboard buckets and why.

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