Window function: running total over date

Last updated: February 16, 2026

Quick Overview

Use window functions to compute rank partitioned by user_id.

ServiceNow
Data Manipulation (SQL/Python)
Data Scientist
ServiceNow
February 16, 2026
Data Scientist
Take-home Project
Data Manipulation (SQL/Python)
Medium

146

7

211 solved


Use window functions to compute rank partitioned by user_id.

Data manipulation questions at ServiceNow test your ability to work with real-world datasets. This Take-home Project question evaluates your SQL proficiency, understanding of data modeling, and ability to derive insights from raw data.

What the Interviewer Expects
  • Use advanced SQL features: window functions, CTEs, subqueries
  • Write efficient queries that avoid common performance pitfalls
  • Handle complex data transformations with multiple joins and aggregations
  • Discuss indexing strategy and query optimization
  • Address data quality issues: duplicates, missing values, outliers
Key Topics to Cover
Pandas vectorized operations and groupby
Date/time manipulation
Index optimization and query performance
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
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
  • How would you validate the correctness of your query results?
  • How would you optimize this query for a table with 100 million rows?
  • How would you handle this if the data was spread across multiple databases?
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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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