# 9 TransUnion SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

TransUnion employees use SQL daily for analyzing financial behaviors by querying large databases and mining customer credit data for predictive modeling. That's the reason behind why TransUnion typically asks SQL query questions in interviews for Data Analyst, Data Science, and BI jobs.

So, to help you practice, we've curated 9 TransUnion SQL interview questions – able to answer them all?

## 9 TransUnion SQL Interview Questions

### SQL Question 1: Calculate Average Credit Score by Product and Month

Suppose that you have a dataset of TransUnion's customers. Each record represents a monthly report of credit score reported for customers who use different products. Write a SQL query to calculate the average credit score by product and month using window function.

##### Example Input:
report_idcustomer_idreport_dateproduct_idcredit_score
123467801/02/2022901234650
456778901/18/2022901234700
234563001/10/2022901234620
768978002/05/2022901234660
901289002/14/2022901234650
567865003/03/2022567890700
921367003/16/2022567890720
##### Example Output:
monthproduct_idavg_credit_score
1901234656.67
2901234655.00
3567890710.00

In this scenario, the window function is used to calculate the average credit score for each month and product. The clause in the window function breaks the data into separate partitions (or groups) for each combination of product and month. Each of these partitions is then used to calculate the average credit score. The result is a list of average credit scores for each product and month.

For more window function practice, try this Uber SQL Interview Question on DataLemur's interactive SQL code editor:

### SQL Question 2: Employees Earning More Than Managers

Given a table of TransUnion employee salaries, write a SQL query to find employees who make more than their own boss.

#### TransUnion Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

#### Example Output:

employee_idemployee_name
3Olivia Smith

This is the output because Olivia Smith earns \$8,000, surpassing her manager, William Davis who earns 7,800.

Solve this question directly within the browser on DataLemur:

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 code above is confusing, you can find a detailed solution here: Highly-Paid Employees.

### SQL Question 3: What are some similarities and differences between unique and non-unique indexes?

{#Question-3}

While both types of indexes improve the performance of SQL queries by providing a faster way to lookup rows of data, a unique index enforces the uniqueness of the indexed columns, meaning that no duplicate values are allowed in the indexed columns. On the other hand, a non-unique index allows duplicate values in the indexed columns.

Here is an example of a unique index on the column of a table of TransUnion employees:

This index would ensure that no two TransUnion employees have the same , which could be used as a unique identifier for each employee.

Here is an example of a non-unique index on the column of the same table:

This index would not enforce uniqueness, but it could be used to improve the performance of queries that filter or sort the data based on the column. For example, if you want to retrieve all employees who were Data Analysts, the database can use the index to quickly locate and retrieve the desired records without having to search through all the employees.

### SQL Question 4: Analyze Credit Score Changes:

TransUnion is a major credit bureau responsible for maintaining credit histories and scores for millions of consumers. Suppose you are given two tables: one representing customers and their credit scores at various dates, and another table representing changes in their credit scores.

Design and write a PostgreSQL query to determine the total score change (positive and negative) for each customer over a given year.

##### Example Input:
01John DoeJohnD
02Jane SmithJaneS
03Jim BrownJimB
##### Example Input:
score_idcustomer_idscore_datecredit_score
10010101/01/2021720
10020101/07/2021730
10030201/01/2021680
10040201/04/2021670
10050301/01/2021700
10060301/06/2021710

This query first uses a common table expression (CTE) with a window function () to get the next score for each customer ordered by date. It then joins the and tables on . It filters by date for scores only in 2021 and then uses to calculate the total change in scores for each customer over the year 2021. The results are ordered by in descending order.

### SQL Question 5: In the context of a database transaction, what does ACID mean?

A transaction is a one or more SQL commands which are executed as a singular unit if the transaction -commits- (or no execution hapens if the transaction -aborts-).

For transactions, a DBMS is supposed to enforce the follwing ACID properties: Atomicity, Consistency, Isolation, & Durability.

Here's what each one means:

• Atomicity: the transaction is completed in an all-or-nothing way (no partial commits)
• Consistency: the transaction is valid and follows all constraints and restrictions
• Isolation: the transaction doesn't affect another transaction
• Durability: the committed transactions is stored permanently in the DB (it doesn't dissapear!)

As you can see, it's pretty important for the multiple databases where TransUnion store's it's data to be ACID-compliant!

### SQL Question 6: Filter Customer Records for Credit Evaluation

TransUnion, a credit reporting agency, is updating its customer evaluation process. The company wants to focus on customers who were born after 1980, have made more than 3 transactions in the last year, and have credit scores over 750. Use SQL to filter out these customers from the database.

Consider the following tables and .

##### Example Input:
customer_idbirth_yearcredit_score
1231975760
2651985790
3621990720
1921982770
9811983780
##### Example Input:
transaction_idcustomer_idtransaction_date
1011232020-07-12
1021232021-06-12
1031232021-12-02
1042652021-02-12
1052652021-05-12
1062652021-11-03
1072652021-12-25
1083622021-01-06
1091922021-06-18
1101922021-07-23
1111922021-08-12
1121922021-10-10
1139812021-10-20

This SQL query first performs an INNER JOIN operation on the customer and transactions tables based on the customer ID, and then keeps only the rows that meet the given criteria. The WHERE clause filters out customers who were born after 1980 and have credit scores higher than 750. Lastly, the HAVING clause ensures that we only include customers who have made more than 3 transactions in the last year.

### SQL Question 7: Why are foreign key's important in databases?

A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables.

To demonstrate this concept, let's analyze TransUnion's marketing analytics database which stores data from Google Ads campaigns:

:

+------------+------------+------------+------------+ | ad_id | campaign_id| keyword | click_count| +------------+------------+------------+------------+ | 1 | 100 | TransUnion pricing | 10 | | 2 | 100 | TransUnion reviews | 15 | | 3 | 101 | TransUnion alternatives | 7 | | 4 | 101 | buy TransUnion | 12 | +------------+------------+------------+------------+

is a foreign key. It references the of the Google Ads campaign that each ad belongs to, establishing a relationship between the ads and their campaigns. This foreign key allows you to easily query the table to find out which ads belong to a specific campaign, or to find out which campaigns a specific ad belongs to.

It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the ad group that each ad belongs to, and the of the Google Ads account that the campaigns belong to.

### SQL Question 8: Calculate the Click-Through-Rate (CTR) for TransUnion's marketing campaign

TransUnion is running a campaign where they send out advertising emails to their customers. For each email sent, TransUnion tracks if the customer clicked the link provided within the email. Each customer can be sent multiple emails in a month, and each email can be clicked multiple times.

You are tasked to calculate the overall Click-Through-Rate (CTR) for TransUnion's email marketing campaign in a particular month. CTR is generally calculated as the total number of Clicked Emails/ total number of Sent Emails.

Here are the tables you can work with:

##### Example Input:
email_idcustomer_idsent_date
10187906/01/2022
10276006/02/2022
10387906/05/2022
10450006/10/2022
10576006/15/2022
##### Example Input:
clicked_idemail_idclick_date
110106/01/2022
210206/03/2022
310106/06/2022
410406/11/2022
510406/16/2022

This query calculates the CTR by first joining the 'emails_sent' and 'emails_clicked' tables on 'email_id' using a LEFT JOIN. That means all emails sent are included in the result, even if they weren't clicked. It then counts the distinct email IDs in each table to get the total number of emails sent and clicked. Finally, it divides the count of clicked emails by the count of sent emails to get the overall CTR. Results are filtered for the month of June 2022 using the EXTRACT function to match the appropriate month from the sent date.

To practice a similar problem about calculating rates, try this TikTok SQL Interview Question within DataLemur's interactive coding environment:

### SQL Question 9: Filter Customer Records

In TransUnion, a global credit reporting agency, the company deals with an extensive database of customers. One day, the business team asked you to filter out the customer records from the database where the customer's first name starts with 'A', and whose email domain is 'gmail.com'. Given the table, write a SQL query to solve this problem.

##### Example Input:
customer_idfirst_namelast_nameemail
2AliceJohnsonahalice@yahoo.com
3AnthonyWilliamsanthonyw@gmail.com
4AndrewJonesajones@hotmail.com
5AmandaBrownamanda.brown@gmail.com
##### Example Output:
customer_idfirst_namelast_nameemail
3AnthonyWilliamsanthonyw@gmail.com
5AmandaBrownamanda.brown@gmail.com

The SQL query uses the keyword to filter out the records based on the pattern provided. Here, the '%' sign is used as a wildcard character that means "zero, one, or multiple characters". The query checks two conditions - first, if the first name of the customer starts with 'A' indicated by and second, whether the email domain of the user is 'gmail.com', indicated by . The query returns all the columns for the records that meet both conditions.

### Preparing For The TransUnion SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the TransUnion SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above TransUnion SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like tech companies and financial services companies like TransUnion.

Each interview question has multiple hints, detailed solutions and best of all, there's an online SQL code editor so you can easily right in the browser your SQL query and have it executed.

To prep for the TransUnion SQL interview you can also be wise to practice SQL questions from other financial services companies like:

However, if your SQL coding skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.

This tutorial covers things like filtering data with boolean operators and finding NULLs – both of which pop up routinely in TransUnion SQL assessments.

### TransUnion Data Science Interview Tips

#### What Do TransUnion Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems to practice for the TransUnion Data Science Interview are:

#### How To Prepare for TransUnion Data Science Interviews?

I believe the best way to prep for TransUnion Data Science interviews is to read the book Ace the Data Science Interview.

It has 201 interview questions taken from tech companies like Google & Microsoft. The book's also got a crash course covering Product Analytics, SQL & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.

While the book is more technical, it's also key to prepare for the TransUnion behavioral interview. Start by understanding the company's cultural values.