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This question evaluates SQL data manipulation and analytical competencies, including aggregation, joins between user and event tables, time-window filtering, deduplication for unique users, and computation of counts and pickup rates.

  • medium
  • Meta
  • Data Manipulation (SQL/Python)
  • Product Analyst

Write Call Analytics SQL Queries

Company: Meta

Role: Product Analyst

Category: Data Manipulation (SQL/Python)

Difficulty: medium

Interview Round: Onsite

Assume you are given two tables. Table: `calls` - `call_id` BIGINT - `sender_id` BIGINT - `receiver_id` BIGINT - `call_time` TIMESTAMP - `pickup` VARCHAR -- values: 'Y' or 'N' - `call_type` VARCHAR -- values: 'video' or 'voice' Table: `users` - `user_id` BIGINT - `country` VARCHAR Assume: - `calls.sender_id` and `calls.receiver_id` both reference `users.user_id`. - All timestamps are stored in UTC. - Unless otherwise stated, use the last 30 full days of data ending at `CURRENT_DATE` in UTC. - A 'caller' means `sender_id`. Write SQL for the following tasks: 1. Return daily call volume and pickup rate by call type for the last 30 days. Required output columns: - `call_date` - `call_type` - `total_calls` - `picked_up_calls` - `pickup_rate` 2. For a specified country (for example, 'US'), compute the percentage of calls initiated by callers in that country that were picked up during the same 30-day window. Required output columns: - `country` - `total_calls` - `picked_up_calls` - `pickup_rate` 3. Compute the percentage of unique callers who made at least one voice call and at least one video call during the same 30-day window. Required output columns: - `total_unique_callers` - `callers_with_both_types` - `pct_callers_with_both_types`

Quick Answer: This question evaluates SQL data manipulation and analytical competencies, including aggregation, joins between user and event tables, time-window filtering, deduplication for unique users, and computation of counts and pickup rates.

Last updated: May 7, 2026

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