Calculate cumulative sum per date

Last updated: September 18, 2025

Quick Overview

Write a query to compute cumulative sum grouped by user, handling edge cases like nulls and duplicates.

PayPal
Data Manipulation (SQL/Python)
Data Scientist
PayPal
September 18, 2025
Data Scientist
Phone Screen
Data Manipulation (SQL/Python)
Easy

2

10

831 solved


Write a query to compute cumulative sum grouped by user, handling edge cases like nulls and duplicates.

This question from PayPal's Phone Screen tests practical data skills. The interviewer wants to see clean, efficient queries that handle edge cases like NULLs, duplicates, and large datasets.

What the Interviewer Expects
  • Write syntactically correct SQL with proper JOIN and WHERE clauses
  • Use GROUP BY and aggregate functions appropriately
  • Handle NULL values correctly in your queries
  • Explain the query execution plan at a high level
Key Topics to Cover
Pandas vectorized operations and groupby
Data cleaning and transformation
Index optimization and query performance
JOIN types and when to use each
How to Approach This
  1. Clarify the schema and expected output format before writing queries.
  2. Use CTEs (WITH clauses) to break complex queries into readable steps.
  3. Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
  4. Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
  5. For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
  • What would you do if this query needs to run every 5 minutes?
  • How would you handle slowly changing dimensions in this scenario?
  • How would you optimize this query for a table with 100 million rows?
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Sample Answer
Approach

Break the problem into logical steps before writing SQL. Think about: 1. What tables do I need to join and on which keys? 2. What filtering (WHERE) d...

Solution Pattern

```sql WITH filtered_data AS ( SELECT * FROM main_table WHERE condition = 'value' AND date_col >= '2024-01-01' ), aggregated AS ( SELECT ...


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