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Data Manipulation (SQL/Python) questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master data manipulation (sql/python) interviews.

What difficulty level is this interview question?

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

What role is this question designed for?

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

Compute percent of active users with 50+ calls

Q: Compute percent of active users with 50+ calls

This question evaluates a data scientist's competence in Data Manipulation (SQL/Python), focusing on SQL aggregation, joining call and user snapshot tables, temporal filtering, and computing user-level engagement metrics across incoming and outgoing call records.

Problem

You work on a Messenger-like app. You want to measure how many active users in Great Britain (GB) today have been heavy callers recently.

Tables

Assume two tables (names can vary).

1) `calls`

One row per call record.

call_id (STRING, primary key)
caller_id (STRING)
receiver_id (STRING)
date (STRING) — call date in string format (e.g., '2025-11-06' )
duration (INT) — call duration (seconds)

2) `user_daily_status`

One row per user per day describing user attributes and activity.

user_id (STRING)
country (STRING)
is_active (INT) — 1 if the user is active on that date, else 0
date (STRING) — snapshot date in string format (e.g., '2025-11-06' )

Definitions / assumptions

“GB active users today” = users with country = 'GB' , is_active = 1 on today’s snapshot date.
“Previous seven days” = the 7-day lookback window excluding today , i.e. [today - 7 days, today - 1 day] .
A user’s “call records” count includes both:
- outgoing calls where the user is the caller_id , and
- incoming calls where the user is the receiver_id .
Count calls as number of call records (e.g., count of call_id ).

Task

Write a SQL query to compute:

the percentage of today’s GB active users who had more than 50 call records in the previous 7 days.

Output

Return at least:

as_of_date
active_users_today (denominator)
active_users_gt_50_calls_last_7d (numerator)
pct_active_users_gt_50_calls_last_7d

(You may assume your SQL dialect provides a way to parse the date string into a date type.)

Problem

You work on a Messenger-like app. You want to measure how many active users in Great Britain (GB) today have been heavy callers recently.

Tables

Assume two tables (names can vary).

1) `calls`

One row per call record.

call_id (STRING, primary key)
caller_id (STRING)
receiver_id (STRING)
date (STRING) — call date in string format (e.g., '2025-11-06' )
duration (INT) — call duration (seconds)

2) `user_daily_status`

One row per user per day describing user attributes and activity.

user_id (STRING)
country (STRING)
is_active (INT) — 1 if the user is active on that date, else 0
date (STRING) — snapshot date in string format (e.g., '2025-11-06' )

Definitions / assumptions

“GB active users today” = users with country = 'GB' , is_active = 1 on today’s snapshot date.
“Previous seven days” = the 7-day lookback window excluding today , i.e. [today - 7 days, today - 1 day] .
A user’s “call records” count includes both:
- outgoing calls where the user is the caller_id , and
- incoming calls where the user is the receiver_id .
Count calls as number of call records (e.g., count of call_id ).

Task

Write a SQL query to compute:

the percentage of today’s GB active users who had more than 50 call records in the previous 7 days.

Output

Return at least:

as_of_date
active_users_today (denominator)
active_users_gt_50_calls_last_7d (numerator)
pct_active_users_gt_50_calls_last_7d

(You may assume your SQL dialect provides a way to parse the date string into a date type.)

Compute percent of active users with 50+ calls

Quick Overview

Problem

Tables

1) `calls`

2) `user_daily_status`

Definitions / assumptions

Task

Output

Submit Your Answer

Compute percent of active users with 50+ calls

Quick Overview

Problem

Tables

1) `calls`

2) `user_daily_status`

Definitions / assumptions

Task

Output

Submit Your Answer

Compute percent of active users with 50+ calls

Quick Overview