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What difficulty level is this interview question?

This is a easy difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at Databricks.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Databricks during technical interviews.

Count weekly customers with ≥$1000 YTD spend

Last updated: May 3, 2026

Quick Overview

This question evaluates proficiency in time-series aggregation, cumulative year-to-date computations, distinct-count deduplication, and use of SQL or pandas for data manipulation and reporting.

Databricks

Dec 3, 2025, 12:00 AM

Data Scientist

Technical Screen

Data Manipulation (SQL/Python)

You are given a transaction-level table and need to compute a weekly time series.

Table

transactions

date (DATE) — transaction date (assume UTC and calendar dates)
transaction_id (STRING/INT) — primary key
customer_id (STRING/INT)
dollars (NUMERIC) — non-negative transaction amount in USD

Definitions

Week : calendar week starting on Monday (you may state a different week boundary, but be consistent).
Year-to-date (YTD) spend as of a week : for a given customer_id and a given week w within year Y , the sum of dollars from Jan 1 of year Y up to and including the last day of week w .

Task

For each week present in the data, compute the number of distinct customers whose YTD spend as of that week is at least $1000.

Output

Return a table with:

week_start (DATE)
num_customers_ge_1000_ytd (INT)

You may solve this in SQL or Python (pandas).

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