Window function: rank over user_id
Last updated: November 19, 2025
Quick Overview
Use window functions to compute rank partitioned by date.
Amazon
November 19, 20258
5
3,182 solved
Use window functions to compute rank partitioned by date.
Data manipulation questions at Amazon test your ability to work with real-world datasets. This Technical Screen 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
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
- Can you rewrite this without using subqueries?
- How would you validate the correctness of your query results?
- 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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Practice SQL ProblemsSample Answer
Problem Understanding
In this problem, we are dealing with a dataset that likely includes user interactions or transactions recorded per date. The goal is to compute a rank for each user based on their activity on each dat...
Approach
To build the query, we will: 1. CTE Creation: Create a Common Table Expression (CTE) to aggregate the data by user_id and activity_date. For example, we might count the number of activities pe...
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