Analyze Group Call Adoption Using SQL Queries

Q: How do I practice SQL interview questions?

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 Technical Screen 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

CALL_LOGS

| call_id | user_id | call_start           | call_end             | is_group_call | participant_cnt |
| 101     | 12      | 2023-08-01 10:00:00  | 2023-08-01 10:15:00  | 0             | 2               |
| 102     | 34      | 2023-08-01 10:05:00  | 2023-08-01 10:30:00  | 1             | 4               |
| 103     | 56      | 2023-08-01 11:00:00  | 2023-08-01 11:20:00  | 1             | 3               |
| 104     | 12      | 2023-08-02 09:00:00  | 2023-08-02 09:10:00  | 0             | 2               |
| 105     | 78      | 2023-08-02 09:15:00  | 2023-08-02 09:40:00  | 1             | 5               |

##### Scenario

A messaging app is launching a new "group call" feature. You have access to historical call data and need to quantify early adoption of the feature.

##### Question

Using the call_logs table, write SQL to:

For each calendar day, return
(a) total calls,
(b) total group calls,
(c) unique callers who initiated a group call, and
(d) percentage of calls that are group calls.

List all days where group-call percentage is below 10%.

##### Hints

DATE(call_start), COUNT(*), SUM(is_group_call), COUNT(DISTINCT CASE WHEN is_group_call=1 THEN user_id END)

PracHub · Accepted Answer

This question evaluates SQL data-manipulation and analytical skills, including aggregation, conditional counting, distinct counts, date-based grouping, and percentage calculations on event logs.

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