How do I approach Data Manipulation (SQL/Python) interview questions?

Data Manipulation (SQL/Python) questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master data manipulation (sql/python) interviews.

What difficulty level is this interview question?

This is a easy difficulty Data Manipulation (SQL/Python) question, commonly asked during Onsite rounds at Apple.

What role is this question designed for?

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

Write queries to compute salary and budget stats

Q: Write queries to compute salary and budget stats

This set of tasks evaluates data manipulation and analytical competencies, specifically the ability to compute order-statistics on numeric columns, perform relational joins to enrich event records, and reason about constrained selection for maximizing hires using SQL and pandas.

You are given the following interview tasks. Write solutions in SQL and/or Python (pandas) as appropriate.

Task 1 — Second highest salary

You have a table:

employees

employee_id INT (PK)
name VARCHAR
salary INT

Return the second highest distinct salary.

If there is no second distinct salary, return NULL .

Required output

second_highest_salary (INT or NULL)

Task 2 — Merge two datasets

You are given two datasets that share a common key:

users

user_id INT (PK)
country VARCHAR
signup_date DATE

user_events

user_id INT (FK → users.user_id)
event_time TIMESTAMP
event_type VARCHAR

Merge them so that each event row is enriched with user attributes.

Keep only events that have a matching user_id in users (inner join).

Required output

user_id , country , signup_date , event_time , event_type

Task 3 — “Run out the budget” (maximize hires)

You have a list/table of employees with their salaries and a total hiring budget B.

Each employee costs exactly their salary .
You can hire at most once per employee.
Goal: maximize the number of employees hired without exceeding the budget.

Input

employees(employee_id, salary) and an integer B

Required output

max_hires (INT)

Clarify any assumptions you need (e.g., what to do if B <= 0).

You are given the following interview tasks. Write solutions in SQL and/or Python (pandas) as appropriate.

Task 1 — Second highest salary

You have a table:

employees

employee_id INT (PK)
name VARCHAR
salary INT

Return the second highest distinct salary.

If there is no second distinct salary, return NULL .

Required output

second_highest_salary (INT or NULL)

Task 2 — Merge two datasets

You are given two datasets that share a common key:

users

user_id INT (PK)
country VARCHAR
signup_date DATE

user_events

user_id INT (FK → users.user_id)
event_time TIMESTAMP
event_type VARCHAR

Merge them so that each event row is enriched with user attributes.

Keep only events that have a matching user_id in users (inner join).

Required output

user_id , country , signup_date , event_time , event_type

Task 3 — “Run out the budget” (maximize hires)

You have a list/table of employees with their salaries and a total hiring budget B.

Each employee costs exactly their salary .
You can hire at most once per employee.
Goal: maximize the number of employees hired without exceeding the budget.

Input

employees(employee_id, salary) and an integer B

Required output

max_hires (INT)

Clarify any assumptions you need (e.g., what to do if B <= 0).

Write queries to compute salary and budget stats

Quick Overview

Task 1 — Second highest salary

Task 2 — Merge two datasets

Task 3 — “Run out the budget” (maximize hires)

Comments (0)

Write queries to compute salary and budget stats

Quick Overview

Task 1 — Second highest salary

Task 2 — Merge two datasets

Task 3 — “Run out the budget” (maximize hires)

Comments (0)