Calculate Response Rate and Compare User Survey Ratings

Q: Calculate Response Rate and Compare User Survey Ratings

This question evaluates data manipulation and analytical skills, including correct SQL join selection, NULL handling, aggregation grain, denominator construction for response rates, and comparative statistics for mean rating differences between cohorts.

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 Onsite 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

USERS

user_id | signup_date

10      | 2024-03-20

11      | 2024-04-01

12      | 2024-04-05

​

SURVEYS

survey_id | user_id | sent_at

1         | 10      | 2024-04-01

2         | 11      | 2024-04-02

3         | 12      | 2024-04-05

​

SURVEY_RESPONSES

survey_id | user_id | responded_at       | rating

1         | 10      | 2024-04-01 10:02   | 4

3         | 12      | 2024-04-05 12:15   | 5

##### Scenario

Using Meta’s notification-survey data, write SQL to
(a) compute the survey response rate and
(b) test whether new users have a higher average survey rating than existing users.

##### Question

Write a query that returns overall response_rate = #responses / #surveys. State and handle your join choice when surveys lack a response. Write a query that compares mean rating between new users (<30 days since signup) and existing users, controlling aggregation level appropriately.

##### Hints

Think join type, denominator, NULL handling, aggregation grain, and division-by-zero safeguards.

PracHub · Accepted Answer

This question evaluates data manipulation and analytical skills, including correct SQL join selection, NULL handling, aggregation grain, denominator construction for response rates, and comparative statistics for mean rating differences between cohorts.

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