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Solve a problem using a window function in Spark or SQL.

SQLmedium0.3 min readPremium
Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Capgemini
Key Concepts Tested
partitionsparksqlwindow
Expert AnswerPremium
68 wordsInterview-ready
Window example—rank per group: SELECT *, ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) rn FROM employees. PySpark: w = Window.partitionBy('dept').orderBy(col('salary').desc()); df.withColumn('rn', row_number().over(w)). Use for: top-N, dedup, running metrics. Combine with filter: WHERE rn <= 3. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Capgemini. The answer also includes follow-up discussion points that interviewers commonly explore.

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