10 TPG SQL Interview Questions (Updated 2024)
At TPG, SQL is used day-to-day for analyzing complex financial datasets for strategic insights and querying databases for data-driven decision making. That's why TPG frequently asks SQL problems in interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you study, we've collected 10 TPG SQL interview questions – can you solve them?
10 TPG SQL Interview Questions
SQL Question 1: Identify the top spending customers in "TPG"
TPG is a telecommunications company that offers various products and services to its customers including internet plans, mobile plans, and other offerings. Your task is to identify the 'whale users' for TPG, the customers who spend the most on TPG products and services. Write a SQL query to identify the top 5 customers who have spent the most in the last 3 months.
Consider the following tables:
Example Input:
| user_id | birthdate | signup_date | |
|---|---|---|---|
| 1001 | 1980-06-10 | 2020-03-10 | person1@example.com |
| 1002 | 1990-03-15 | 2020-05-17 | person2@example.com |
| 1003 | 2000-12-20 | 2021-01-11 | person3@example.com |
| 1004 | 1985-04-27 | 2021-05-22 | person4@example.com |
| 1005 | 1975-10-31 | 2020-10-13 | person5@example.com |
Example Input:
| transaction_id | user_id | transaction_amount | transaction_date |
|---|---|---|---|
| 5001 | 1001 | 100 | 2022-09-30 |
| 5002 | 1002 | 200 | 2022-08-15 |
| 5003 | 1001 | 150 | 2022-10-01 |
| 5004 | 1003 | 50 | 2022-07-25 |
| 5005 | 1005 | 300 | 2022-06-30 |
In the above, transaction_amount represents the amount spent by the user in a single transaction, and the transaction_date represents the date when the transaction occurred.
Answer:
This query first joins the table with the table on the common field. The clause is then used to filter out the transactions that occurred in the last 3 months. The aggregate function is used along with to calculate the total amount spent by each user in this period. The results are then ordered in descending order based on the total amount spent to identify the top spending customers, and the clause is used to display only the top 5 spenders.
To solve a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
SQL Question 2: Highly-Paid Employees
Suppose you had a table of TPG employee salaries. Write a SQL query to find the employees who earn more than their direct manager.
TPG Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Test your SQL query for this interview question and run your code right in DataLemur's online SQL environment:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the solution above is confusing, you can find a detailed solution with hints here: Employee Salaries Higher Than Their Manager.
SQL Question 3: What does adding 'DISTINCT' to a SQL query do?
The clause is used to remove all duplicate records from a query.
For example, if you had a table of open jobs TPG was hiring for, and wanted to see what are all the unique job titles that were currently available at the company, you could write the following query:
SQL Question 4: Average Revenue per Product per Month
Imagine you are a data analyst at TPG (an investment company). The investment team is interested in analyzing the performance of the products in which the company has invested. An important metric they are interested in is the average monthly revenue per product over a certain time period.
The dataset you have been given to analyze is in table sales, which includes all transactions made for all products. The sales table has the following columns:
sale_id: A unique ID for each sale.
product_id: The ID of the product sold.
sale_date: The date of the sale.
revenue: The amount of revenue made from this sale.
The team would like you to create an SQL query that will provide the average monthly revenue for each product.
Example Input:
| sale_id | product_id | sale_date | revenue |
|---|---|---|---|
| 0001 | A1 | 01/10/2020 | 1000 |
| 0002 | B1 | 01/10/2020 | 2000 |
| 0003 | A1 | 01/15/2020 | 1500 |
| 0004 | C1 | 02/20/2020 | 1200 |
| 0005 | A1 | 03/15/2020 | 1600 |
| 0006 | B1 | 03/20/2020 | 2500 |
| 0007 | C1 | 03/22/2020 | 1300 |
Answer:
In this query, is used to extract the month from the sale date. The function is combined with a clause to compute the average monthly revenue for each product. The clause (which signifies a window function) operates on each "window" of rows defined by the clause.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
Read about TPG and see what values and missions the company upholds.
SQL Question 5: Can you explain the distinction between a clustered and a non-clustered index?
Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 6: Analyzing Click-through Conversion Rates for TPG
TPG (Tech Product Giants), a leading company that sells digital products, wants to analyze the click-through conversion rates from viewing a product to adding it to the cart. Here are two tables showcasing the and actions that customers performed on certain dates.
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 101 | 345 | 06/08/2022 00:00:00 | 60001 |
| 102 | 567 | 06/10/2022 00:00:00 | 69852 |
| 103 | 345 | 06/16/2022 00:00:00 | 60001 |
| 104 | 789 | 07/20/2022 00:00:00 | 69852 |
| 105 | 234 | 07/25/2022 00:00:00 | 60001 |
Example Input:
| cart_id | user_id | add_date | product_id |
|---|---|---|---|
| 1101 | 345 | 06/09/2022 00:00:00 | 60001 |
| 1102 | 567 | 06/11/2022 00:00:00 | 69852 |
| 1103 | 789 | 07/26/2022 00:00:00 | 69852 |
| 1104 | 234 | 07/30/2022 00:00:00 | 60001 |
For a given month, calculate the click-through conversion rates, i.e., the number of 'add to carts' per 'view' for each product.
Answer:
This query will give the click-through conversion rates per product for each month. It calculates the conversion rate by dividing the number of unique 'add to cart' actions by the number of unique 'views', for each product per month. The 'view' and 'add to cart' actions are joined on user_id and product_id, with the condition that the 'add to cart' action date is after the 'view' date.
To solve a related SQL interview question on DataLemur's free interactive SQL code editor, attempt this Facebook SQL Interview question:
SQL Question 7: How do the and window functions differ from each other?
In SQL, both and are used to rank rows within a result set. The key difference between the two functions is how deal with two or more rows having the same value in the ranked column (aka how the break ties).
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the second row, and a rank of 4 to the third row.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
SQL Question 8: Locate specific customers from TPG
TPG has a huge database of its customers. They want you to extract the records of customers whose names begin with the letter 'M' as they want to run a specific targeted marketing campaign for these customers. Write a SQL query that helps TPG find these customers.
Example Input:
| customer_id | first_name | last_name | date_of_birth | |
|---|---|---|---|---|
| 1751 | Mark | Jones | 05/22/1995 | mark.jones@email.com |
| 2562 | Michelle | Davis | 09/10/1989 | michelle.davis@email.com |
| 3912 | Sam | Brown | 12/19/1992 | sam.brown@email.com |
| 4253 | Matthew | Baker | 02/26/1997 | matthew.baker@email.com |
| 6129 | Jessica | Smith | 08/14/1996 | jessica.smith@email.com |
Answer:
This query will return all the rows in 'customers' table where 'first_name' begins with 'M'. The '%' symbol is a wildcard that matches any sequence of characters. The 'LIKE' keyword is used in the WHERE clause to search for the specified pattern in a column. So 'M%' is a pattern that matches any string that starts with 'M'.
SQL Question 9: Customer Subscription Analysis
As a data analyst for TPG, a telecom company, you've been given two tables, and . The table has columns , , , and . The table has columns , , , and .
The company offers three subscription plans that are represented by : 1 for "Basic", 2 for "Regular", and 3 for "Premium".
Write a SQL query to analyze the data and find out the number of customers who joined in 2022 and their respective subscription plans. Include customers who haven't subscribed to any plan yet.
Example Input
| cust_id | first_name | last_name | join_date |
|---|---|---|---|
| 1001 | John | Doe | 2022-04-10 |
| 1002 | Jane | Doe | 2022-02-20 |
| 1003 | Marry | Jane | 2022-01-15 |
| 1004 | Peter | Parker | 2022-03-30 |
| 1005 | Bruce | Wayne | 2021-12-20 |
Example Input
| sub_id | cust_id | plan_id | sub_start_date |
|---|---|---|---|
| 4001 | 1001 | 1 | 2022-04-12 |
| 4002 | 1002 | 2 | 2022-02-22 |
| 4003 | 1003 | 3 | 2022-01-18 |
| 4004 | 1004 | 3 | 2022-03-31 |
Answer:
In this query, we're joining the table with the table based on the . The WHERE clause filters out the customers who joined in 2022. The CASE statement converts the into human-readable plan names. The LEFT JOIN ensures that we include customers who haven't subscribed to any plan yet.
Because joins come up frequently during SQL interviews, try this Snapchat Join SQL question:
SQL Question 10: Would a UNION ALL and a FULL OUTER JOIN produce the same result?
No, in 99% of normal cases a and do NOT produce the same result.
You can think of more as set addition when it combines the results of two tables.
You can think of a more as set multiplication, producing all combos of table 1 with combos of table 2.
TPG SQL Interview Tips
The best way to prepare for a TPG SQL interview is to practice, practice, practice.
In addition to solving the earlier TPG SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there's an online SQL code editor so you can instantly run your query and have it executed.
To prep for the TPG SQL interview it is also a great idea to solve SQL questions from other investment management and private equity companies like:
In case your SQL coding skills are weak, forget about going right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers SQL topics like handling dates and filtering strings using LIKE – both of these pop up frequently in SQL interviews at TPG.
TPG Data Science Interview Tips
What Do TPG Data Science Interviews Cover?
Besides SQL interview questions, the other types of questions to practice for the TPG Data Science Interview include:
- Statistics & Probability Questions
- Python or R Coding Questions
- Analytics and Product-Metrics Questions
- ML Interview Questions
- Resume-Based Behavioral Questions
How To Prepare for TPG Data Science Interviews?
To prepare for TPG Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG & startups
- a refresher on SQL, Product-Sense & ML
- over 1000+ 5-star reviews on Amazon