Determine Old vs. New Users' Shop Visibility Changes

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PracHub provides an interactive SQL console where you can write and test queries against real database schemas. Get instant feedback and compare your solution with the expected output.

Q: What difficulty level is this coding question?

This is a Medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Onsite rounds at Meta.

Q: What role is this question designed for?

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

Question

SHOP_VISIBILITY_HISTORY

+----------+----------------+---------------------+-------------------+---------+
| user_id  | user_signup_dt | action_timestamp    | visibility_status | shop_id |
+----------+----------------+---------------------+-------------------+---------+
| 101      | 2020-03-10     | 2023-07-01 10:04:11 | invisible         | 555     |
| 102      | 2023-06-20     | 2023-07-02 08:22:37 | visible           | 556     |
| 103      | 2021-11-05     | 2023-07-01 14:15:02 | invisible         | 557     |
| 104      | 2023-07-01     | 2023-07-02 16:45:09 | invisible         | 555     |
| 105      | 2020-01-18     | 2023-07-03 09:30:00 | visible           | 558     |
+----------+----------------+---------------------+-------------------+---------+

##### Scenario

E-commerce platform wants to know whether existing users are more likely than newly registered users to set a shop profile to invisible.

##### Question

Given historical shop visibility actions, write a SQL query that computes a metric of your choice that compares the propensity of old users versus new users to switch a shop profile to invisible. Briefly justify why the metric you chose is appropriate.

##### Hints

Define "old" vs "new" users, then calculate the proportion of invisible actions per group.

PracHub · Accepted Answer

This question evaluates SQL-focused data manipulation skills and analytical competency in cohort definition and metric selection for comparing behavioral propensities, specifically assessing how to measure the likelihood of old versus new users switching a shop profile to invisible.

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