Write a query to find conversion rate from sessions
Last updated: April 12, 2026
Quick Overview
Write a SQL query to calculate conversion rate from the sessions table, considering nulls and duplicates.
PayPal
April 12, 202657
3
993 solved
Write a SQL query to calculate conversion rate from the sessions table, considering nulls and duplicates.
PayPal asks this during the Take-home Project because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.
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
- 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?
- Can you rewrite this without using subqueries?
- How would you handle slowly changing dimensions in this scenario?
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Practice SQL ProblemsSample Answer
Problem Understanding
The goal is to calculate the conversion rate from the sessions table, which typically contains records of user sessions on the PayPal platform. Each session may have a corresponding conversion event...
Approach
- Identify the relevant data: The
sessionstable should contain session IDs, timestamps, and possibly flags indicating whether a session resulted in a conversion. - Data cleaning: Handle ...
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