10 Q2 Holdings SQL Interview Questions (Updated 2024)
At Q2, SQL is used often for analyzing financial data for patterns and managing databases to ensure secure real-time banking transactions. That's the reason behind why Q2 Holdings typically asks SQL questions during interviews for Data Science and Data Engineering positions.
To help you prepare for the Q2 Holdings SQL interview, we'll cover 10 Q2 SQL interview questions – can you answer each one?
10 Q2 SQL Interview Questions
SQL Question 1: Identifying Top Users Based on Transactional Activity at Q2 Holdings
Let's assume that Q2 Holdings is a company that provides an online platform for business transactions. We have two tables, and . table contains details about all the registered users, and table keeps records of all the transactions occurred.
A VIP user can be defined as a user who has completed more than a certain number of transactions, say 1000, in a given period, say within a month.
Write a SQL query to identify these VIP users from the database.
Example Input:
| user_id | name | registration_date |
|---|---|---|
| 101 | John Doe | 2018-01-01 |
| 102 | Jane Doe | 2018-02-01 |
| 103 | Alice Gibson | 2019-03-01 |
| 104 | Robert Miles | 2020-04-01 |
Example Input:
| transaction_id | user_id | transaction_date | amount |
|---|---|---|---|
| 201 | 101 | 2022-06-01 | 100 |
| 202 | 101 | 2022-06-02 | 150 |
| 203 | 102 | 2022-06-03 | 200 |
| 204 | 103 | 2022-06-04 | 250 |
| 205 | 104 | 2022-07-01 | 300 |
You could expect output like:
Example Output:
| name | total_transactions |
|---|---|
| Jane Doe | 1233 |
| John Doe | 1120 |
Answer:
In this query, we join with on and then filter the data to include only transactions from the past month. Next, we group by the and count the number of transactions for each user. We then specify our VIP user criterion in the clause i.e., the count of transactions should be more than 1000. Finally, we order our results by the total number of transactions in descending order.
To solve a similar power-user data analysis problem question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:
SQL Question 2: Analyzing Transaction Volume with Window Functions.
Assume you are given a dataset of financial transactions for Q2 Holdings. Each row in the dataset represents a single transaction by a customer with the transaction timestamp and the transaction amount. Your job is to write a SQL query that calculates the weekly transaction volume (sum of transaction amounts) as well as a running total of transaction volume for each week over the last 52 weeks.
Example Input:
| trans_id | customer_id | trans_date | trans_amount |
|---|---|---|---|
| 23891 | 546 | 2022-06-20 | 1050.50 |
| 38291 | 234 | 2022-06-21 | 2500.00 |
| 45612 | 789 | 2022-06-23 | 3450.75 |
| 23894 | 546 | 2022-06-27 | 2050.00 |
| 49284 | 981 | 2022-06-30 | 4500.00 |
Answer:
In the above query, we first create a CTE that groups transactions by week and calculate the sum of transaction amounts as . Then, in the main query, we use a window function to calculate a running total of . The clause in the window function indicates the order in which rows are processed. The clause indicates for each row, all previous rows plus the current row are used to calculate the running total. Finally, we order the result by for easier analysis.
To solve a similar window function question on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:
SQL Question 3: When would you use a clustered index vs. a non-clustered index?
A clustered index is an index where the order of the rows in the database corresponds to the order of the rows in the index. Because of this, a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between the two is that the database tries to keep the data in the same order as the corresponding keys in the clustered index. This can improve the performance of most query operations, as it provides a linear-access path to the data stored in the database.
SQL Question 4: Average Number of Transactions per Client for Q2 Holdings
As a financial technology company, Q2 Holdings serves financial institutions and their clients with online and mobile banking services. One aspect they may want to understand is the average number of transactions per customer. The question could be: "Find the average number of transactions per customer for the last quarter."
Example Input:
| client_id | client_name |
|---|---|
| 1 | Client A |
| 2 | Client B |
| 3 | Client C |
Example Input:
| transaction_id | client_id | transaction_date | amount |
|---|---|---|---|
| 101 | 1 | 2022-07-01 | 500.00 |
| 102 | 1 | 2022-07-15 | 200.00 |
| 103 | 1 | 2022-08-10 | 100.00 |
| 104 | 2 | 2022-07-03 | 700.00 |
| 105 | 3 | 2022-07-05 | 250.00 |
| 106 | 3 | 2022-08-01 | 150.00 |
| 107 | 3 | 2022-09-10 | 300.00 |
Answer:
This query first counts the number of transactions for each client within the last quarter. It then calculates the average of these transaction counts, displaying it alongside the client name. In the case of Q2 holdings, this would provide them with a clearer understanding of the transaction frequency among their clients.
SQL Question 5: Can you explain the difference between the and window functions in SQL?
While both and are used to rank rows, the key difference is in how they deal with 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 2nd row in the tie, and a rank of 4 to the the 3rd tie.
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.
Suppose we had data on how many deals different salespeople at Q2 Holdings closed, and wanted to rank the salespeople.
The result of this query would be:
| name | deals_closed | rank | dense_rank |
|---|---|---|---|
| Akash | 50 | 1 | 1 |
| Brittany | 50 | 2 | 1 |
| Carlos | 40 | 3 | 2 |
| Dave | 40 | 4 | 3 |
| Eve | 30 | 5 | 3 |
| Frank | 20 | 6 | 4 |
As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.
SQL Question 6: Calculating the Average Transaction Amount
Q2 Holdings provides digital banking and lending solutions including lending platforms, personal finance management tools, and digital payments. Let's say you are asked to calculate the average transaction amount per banking platform for 2022 Q1.
Consider the following and tables:
Example Input:
| transaction_id | platform_id | transaction_date | amount |
|---|---|---|---|
| 1001 | 200 | 01/15/2022 | 500 |
| 1002 | 201 | 02/20/2022 | 800 |
| 1003 | 200 | 01/25/2022 | 300 |
| 1004 | 202 | 03/30/2022 | 700 |
| 1005 | 201 | 02/10/2022 | 400 |
Example Input:
| platform_id | platform_name |
|---|---|
| 200 | Digital Banking |
| 201 | Lending Platform |
| 202 | Payment Portal |
Example Output:
| platform_name | avg_amount |
|---|---|
| Digital Banking | 400 |
| Lending Platform | 600 |
| Payment Portal | 700 |
The above output table shows average transaction amounts per banking platform for 2022 Q1.
Answer:
This query first joins the and tables on their common column . Then it filters the records from Q1 of 2022. Finally, it groups the result by from the table and calculates the average transaction amount for each group.
SQL Question 7: How do you identify records in one table that are not present in a second table?
To identify records in one table that do not appear in another, you can use a LEFT JOIN and examine NULL values in the right-side table.
Say for example you had exported Q2 Holdings's CRM (Customer Relationship Management) database into PostgreSQL, and had a table of sales leads, and a second table of companies.
Here's an example of how a query could find all sales leads that are not associated with a company:
This query brings back all rows from the sales leads table, along with any matching rows from the companies table. If there is no matching row in the companies table, NULL values will be returned for all of the right table's columns.
We then filter out out any rows where the column is , leaving only the sales leads that are NOT associated with a company.
SQL Question 8: Find Customers in Q2 Holdings Database
Given the Q2 holdings customer database, find all the customers from the table whose starts with "A".
Example Input:
| customer_id | Full_Name | Address | Date_of_Join | |
|---|---|---|---|---|
| 351 | Adam Smith | 123 Main St, Austin, TX | adamsmith@email.com | 06/08/2020 00:00:00 |
| 372 | Anna Johnson | 456 King St, Austin, TX | annajohnson@email.com | 06/10/2020 00:00:00 |
| 535 | Bill Gate | 789 Avenue St, Austin, TX | billgate@email.com | 06/18/2020 00:00:00 |
| 169 | Alica White | 321 Broad St, Austin, TX | alicawhite@email.com | 07/26/2020 00:00:00 |
| 907 | Charles Peter | 654 Peak St, Austin, TX | charlespeter@email.com | 07/05/2020 00:00:00 |
Answer:
Here is the SQL block containing the answer:
This SQL query will filter the table to find any record where starts with "A". The percentage symbol is a wildcard in SQL that matches any sequence of characters, so 'A%' matches any string starting with "A". The result will be all customers whose names start with "A".
SQL Question 9: Calculate the Total Revenue for Each Product
As an analyst at Q2 Holdings, you are given two tables, and . The table contains information about each product sold, including the , , , and . The table includes the and .
Write a SQL query to join these two tables and calculate the total revenue generated by each product.
Example Input:
| sale_id | customer_id | sale_date | product_id | sale_price |
|---|---|---|---|---|
| 101 | 789 | 01/15/2022 | 9001 | 50.99 |
| 102 | 456 | 02/20/2022 | 9001 | 50.99 |
| 103 | 333 | 03/22/2022 | 9002 | 25.49 |
| 104 | 123 | 04/14/2022 | 9003 | 75.30 |
| 105 | 222 | 05/28/2022 | 9002 | 25.49 |
Example Input:
| product_id | product_name |
|---|---|
| 9001 | Product A |
| 9002 | Product B |
| 9003 | Product C |
Answer:
This SQL query performs an INNER JOIN between and tables using the column as the common column between the two tables. It then uses the function to calculate the total revenue made by each product by summing the for each product. The clause is used to group the revenues by each unique .
Because joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
SQL Question 10: What does / SQL commands do?
Note: interviews at Q2 Holdings often aren't trying to test you on a specific flavor of SQL. As such, you don't need to exactly know that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle – you just need to know the general concept!
Your answer should mention that the / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Here's a PostgreSQL example of using EXCEPT to find all of Q2 Holdings's Facebook video ads with more than 50k views that aren't also being run on YouTube:
If you want to retain duplicates, you can use the EXCEPT ALL operator instead of EXCEPT. The EXCEPT ALL operator will return all rows, including duplicates.
Q2 Holdings SQL Interview Tips
The best way to prepare for a Q2 Holdings SQL interview is to practice, practice, practice.
Besides solving the earlier Q2 Holdings SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each SQL question has multiple hints, detailed solutions and most importantly, there's an interactive SQL code editor so you can right online code up your SQL query and have it graded.
To prep for the Q2 Holdings SQL interview it is also useful to practice SQL problems from other tech companies like:
But if your SQL query skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers topics including AND/OR/NOT and handling date/timestamp data – both of these come up often during Q2 Holdings SQL interviews.
Q2 Data Science Interview Tips
What Do Q2 Holdings Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions covered in the Q2 Holdings Data Science Interview are:
- Probability & Statistics Questions
- Coding Questions in Python or R
- Product Analytics Questions
- ML Modelling Questions
- Behavioral Interview Questions
How To Prepare for Q2 Holdings Data Science Interviews?
To prepare for Q2 Holdings Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG tech companies
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