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

58 practiced

Using NTH_VALUE, return the 3rd highest score per exam. Table: exam_scores(exam_id, student_id, score). Provide SQL and explain how frame specification affects whether the nth value is computed relative to the entire partition or the current row. Use an example where frame choice changes the output.

HardTechnical

69 practiced

Create SQL that returns top 3 customers per region by revenue, but if the 3rd place is tied among multiple customers, break ties deterministically using created_at and customer_id so results are stable across runs. Explain why deterministic tie-breaking matters for BI dashboards.

EasyTechnical

63 practiced

When should you choose window functions over GROUP BY plus joins in a BI report? Give two concrete examples where window functions improve correctness or readability, and one scenario where GROUP BY with a join is preferable for performance.

MediumTechnical

67 practiced

Write a SQL query (specify dialect) to return the top 3 products by revenue per category, but include all products tied at the 3rd rank (i.e., return more than 3 if ties). Table: products(product_id, category_id), sales(product_id, revenue). Use window functions and show how ties are handled.

MediumTechnical

59 practiced

Use NTILE to bucket users into 10 deciles by spend. Table: user_spend(user_id, spend). Write SQL that creates decile labels and discuss how NTILE behaves with duplicates and skewed distributions. Compare NTILE to percentile_cont or approximate_quantiles for very large datasets.

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