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

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

Compute Heavy-Caller Percentages | Meta Interview Question

Q: Compute Heavy-Caller Percentages

This question evaluates proficiency in time-windowed data aggregation, user-level counting and percentage calculations using SQL or Python for analytics tasks.

You are given two tables that track voice calls and daily active users for a messaging app.

Table: call_events

call_id BIGINT — unique call identifier
event_time TIMESTAMP — time when the call started, stored in UTC
caller_user_id BIGINT — user who initiated the call
recipient_user_id BIGINT — user who received the call
country_code STRING — country of the call session, using ISO-style country codes such as GB

Table: daily_active_users

activity_date DATE — date of user activity in UTC
user_id BIGINT — active user identifier
country_code STRING — user country on that date

Assume:

The analysis date is the most recent date available in daily_active_users .
The phrase "last 7 days" means the 7-day window ending on the analysis date, inclusive.
For call-volume calculations, each row in call_events represents one completed call attempt.
For question 2, a user's total call count includes both roles: calls made as caller and calls received as recipient.
GB refers to users in Great Britain.

Write SQL for the following two tasks:

Compute the percentage of distinct callers who made at least 20 calls in the last 7 days.
- A "caller" is defined using caller_user_id only.
- Output columns:
  - analysis_date DATE
  - pct_callers_ge_20 DOUBLE
Compute the percentage of GB daily active users on the analysis date who had at least 50 total calls in the last 7 days.
- Total calls = calls as caller_user_id + calls as recipient_user_id .
- The denominator should be GB users who are active on the analysis date in daily_active_users .
- Output columns:
  - analysis_date DATE
  - pct_gb_dau_ge_50_calls DOUBLE