Calculate retention rate per date

Last updated: August 1, 2025

Quick Overview

Write a query to compute retention rate grouped by date, handling edge cases like nulls and duplicates.

Anthropic
Data Manipulation (SQL/Python)
Data Scientist
Anthropic
August 1, 2025
Data Scientist
Take-home Project
Data Manipulation (SQL/Python)
Medium

37

15

3,932 solved


Write a query to compute retention rate grouped by date, handling edge cases like nulls and duplicates.

This question from Anthropic's Take-home Project 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
  • 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
Common Table Expressions (CTEs)
Aggregate functions and GROUP BY
Data cleaning and transformation
Pandas vectorized operations and groupby
Subqueries and correlated subqueries
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 indexes would you create to support this query?
  • How would you handle this if the data was spread across multiple databases?
  • How would you validate the correctness of your query results?
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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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