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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.

EasyTechnical
0 practiced
You have a session_events(user_id, session_id, event_time, event_name) table. Write a query that returns for each session the first event, last event, first_event_time, and last_event_time using FIRST_VALUE() and LAST_VALUE(). Be explicit about the OVER clause and frame specification so LAST_VALUE returns the true last value for the session.
MediumTechnical
0 practiced
Write a query to find the top 3 orders by amount for each user given orders(order_id, user_id, amount, order_date). Explain two different implementations (ROW_NUMBER() filtering in an outer query and a lateral/subquery approach) and discuss trade-offs in terms of readability and performance for a data warehouse with millions of users.
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.
MediumTechnical
0 practiced
Given a heavy GROUP BY query that computes per-customer weekly totals and then computes a 4-week moving average, demonstrate how to restructure the query using window functions and CTEs to avoid repeated aggregation steps and reduce data shuffled across nodes in a data warehouse.
HardTechnical
0 practiced
Compare how Postgres, Snowflake, and BigQuery differ in: default frame semantics for ORDER BY in windows, support for RANGE on timestamps, support for ordered-set aggregates as window functions, and parallelism/partitioning behavior. For a migration of a Postgres analytic query heavy on LAST_VALUE and RANGE frames to BigQuery, list concrete changes you'd expect to make.

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