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This question evaluates SQL data manipulation and analytical querying skills, including time-window filtering, joins between user and call tables, role-aware event counting (caller vs recipient), distinct user aggregation, and cohort percentage calculations.

  • medium
  • Meta
  • Data Manipulation (SQL/Python)
  • Data Scientist

Count Recent High-Volume Call Users

Company: Meta

Role: Data Scientist

Category: Data Manipulation (SQL/Python)

Difficulty: medium

Interview Round: Technical Screen

Given the following tables: users - user_id BIGINT - country STRING - is_active BOOLEAN calls - call_id BIGINT - initiated_at TIMESTAMP - caller_id BIGINT - recipient_id BIGINT - call_type STRING -- values: 'audio' or 'video' - status STRING -- values such as 'completed', 'missed', 'failed' Assumptions: - users.user_id joins to calls.caller_id and calls.recipient_id. - All timestamps are stored in UTC. - 'Last 7 days' means [analysis_ts - INTERVAL '7' DAY, analysis_ts). - Only completed calls should be counted. - For the second task, a user's video call count should include calls where the user was either the caller or the recipient. Write SQL for the following two tasks: 1. Return the number of distinct users who initiated more than 10 calls in the last 7 days. 2. Return the percentage of active users from Great Britain (country = 'gb') who participated in more than 50 video calls in the last 7 days. Required outputs: - Query 1: initiated_call_users_over_10 - Query 2: pct_active_gb_users_over_50_video_calls

Quick Answer: This question evaluates SQL data manipulation and analytical querying skills, including time-window filtering, joins between user and call tables, role-aware event counting (caller vs recipient), distinct user aggregation, and cohort percentage calculations.

Last updated: May 7, 2026

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