# 11 Equifax SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

At Equifax, SQL is used for data extraction from large databases, including credit reports and public records, as well as querying structured customer credit data, such as credit scores and payment histories, for insights to support decisions. That is why Equifax asks SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.

Thus, to help you practice, here's 11 Equifax SQL interview questions – can you answer each one?

## 11 Equifax SQL Interview Questions

### SQL Question 1: Calculate Risk Scores by Customer and Month

Assuming Equifax used SQL for internal analytics, one common task might be to calculate the average risk score for each customer on a monthly basis, and compare it to the previous month's average. For the purpose of this exercise, assume that Equifax calculates a 'risk score' for each customer each day. A lower risk score is better.

##### Example Input:
risk_score_idcustomer_idscore_daterisk_score
719212008/01/2022667
784125008/02/2022678
539732008/03/2022682
639418708/04/2022676
423892008/05/2022681
720012008/06/2022669
785925008/07/2022681
##### Question Details:

Write a SQL query for PostgreSQL to calculate the average risk score for each customer in every month, and also calculate the difference from the previous month.

For ease of comparison, the months could be represented as integers (e.g., 1 for January, 2 for February, etc.). The output should include customer_id, month, average risk score for the month, and difference from the previous month. Return all rows sorted by customer_id and month in ascending order.

#### Explanation:

In this query, we first calculate the monthly average risk score for each customer using a window function in the CTE. The window function partitions the data by and and then calculates the average score within each partition.

Next, in the CTE, we calculate the month-on-month difference in average risk scores for each customer. Here, we again use a window function, but this time we're using the function to get the previous row's in the ordered sequence of rows for each customer.

Finally, we select all rows from , ordering the output by and .

To practice a similar window function question on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:

### SQL Question 2: Employee Salaries Higher Than Their Manager

Assume there was a table of Equifax employee salaries. Write a SQL query to find the employees who earn more than their own manager.

#### Equifax 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.

You can solve this question interactively 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 tough, you can find a detailed solution with hints here: Employees Earning More Than Managers.

### SQL Question 3: What would you do to optimize a SQL query that was running slow?

First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. You might discover that your query is inefficient, or that there are many database writes at the same time you are doing a read, or maybe too many people are concurrently running queries on the same database server.

For Data Analyst and Data Science positions, knowing the ins-and-outs of SQL performance tuning is out-of-scope for the SQL interview round. However, knowing that joins are expensive, and indexes can speed up queries, is generally enough of an answer for Equifax SQL interviews.

### SQL Question 4: Performance Analysis of Credit Scores

You are asked to help Equifax analyze the performance of credit scores they assign. The database contains two tables, and . The table shows the credit score Equifax assigned to each user, and the table shows which users defaulted on loans. Equifax wants to measure if people with lower credit scores are more likely to default on loans.

Here is some sample data:

##### Example Input:
user_idcredit_score
123750
265650
362800
192600
981700
##### Example Input:
user_iddefaulted
1230
2651
3620
1921
9810

Question: Write a SQL query that calculates the average credit score for users who defaulted and users who did not default.

This SQL query joins the two tables on the user_id field and then groups the data by the defaulted field. For each group (defaulted and not defaulted), it calculates the average credit score. This should provide a clear indication of whether users who default have a lower average credit score compared to users who do not default.

### SQL Question 5: What's the difference between a one-to-one vs. a one-to-many relationship between two entities? Give examples.

In database schema design, a one-to-one relationship between two entities is where each entity is associated with only one instance of the other entity. For example, the relationship between a car and a license plate is one-to-one, because each car can only have one licensce plate, and each licensce plate belongs to exactly one car.

On the other hand, a one-to-many relationship is where one entity can be associated with multiple instances of the 2nd entity. For example, a teacher can teach multiple classes, but each class is associated with only one teacher.

### SQL Question 6: Filter Consumer Records for Marketing Analysis

Equifax, a multinational consumer credit reporting agency, needs your help. You have been given a consumer records database that records yearly income, consumer spending habits, and the credit score of each individual. Your task is to write a SQL query that will filter down this database, returning rows for consumers who have a yearly income of more than \$50,000, a credit score of more than 700 and their spending habit is 'High'. This information will be critical for the marketing department to focus their efforts effectively.

##### Example Input:
consumer_idyearly_incomecredit_scorespending_habit
43675\$40,000680Low
15682\$75,000710High
19845\$55,000705Medium
32468\$87,000758High
46921\$45,000695Low
##### Example Output:
consumer_idyearly_incomecredit_scorespending_habit
15682\$75,000710High
32468\$87,000758High

In this query, we are returning all columns for rows in the table where the consumer's yearly income exceeds \$50,000, their credit score is above 700, and their spending habit is 'High'. This is done using a combination of and operators to filter the data according to the specified conditions. These consumers could be the main target for the marketing team, as they might have the potential for bigger purchases or investments given their high income and spending pattern.

### SQL Question 7: What's the operator do, and can you give an example?

The UNION operator combines the results from multiple SELECT statements into a single result set.

Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Equifax's Facebook ads and their Google ads:

This statement would return a combined result set of both Facebook and Google ads that have a spend greater than \$200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.

Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.

### SQL Question 8: Find the Average Credit Score Across Different Regions.

As the database assistant for Equifax, you have been tasked with finding the average credit score across different regions. You are required to use the AVG function to compile this data. Your sample dataset includes information from the and tables.

##### Example Input:
customer_idfirst_namelast_nameregion
6171JohnDoeNorth
7802JaneSmithSouth
5293SaraLeeEast
4517BettyDavisNorth
##### Example Input:
report_idcustomer_idscore
10016171720
10027802680
10035293800
10046352760
10054517700

The above PostgreSQL query first joins the and tables based on common customer_id. Then, it groups the results by region and calculates the average credit score for each region using the AVG function. This average score is given a new alias called 'average_score', which is shown in the output.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for averaging data based on categories or this Alibaba Compressed Mean Question which is similar for < calculating mean for different group.

### SQL Question 9: Calculate the Click-Through Conversion Rates for Equifax's Products

Equifax, a renowned data analytics company, wants to analyze the click-through conversion rates for their digital products. They are interested in understanding the rate at which users who viewed a product information page went on to add that product to their cart.

They track the following events in two tables and .

##### Example Input:
user_idproduct_idview_date
1235000106/08/2022 00:00:00
2656985206/10/2022 00:00:00
2655000106/18/2022 00:00:00
1926985207/26/2022 00:00:00
9816985207/05/2022 00:00:00
##### Example Input:
2655000106/18/2022 00:00:00
9816985207/05/2022 00:00:00

Write a PostgreSQL query that will calculate the click-through conversion rate for each product, defined as the number of users who added the product to the cart after viewing it, divided by the total number of users who viewed the product, for the month of June.

This query works by joining the and tables on the and fields, such that each row represents a user's action on a product. It then filters for only views that occurred in June, and calculates the conversion rate by dividing the number of distinct users who added the product to their cart after viewing it by the number of distinct users who viewed the product.

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

### SQL Question 10: What's the difference between and clause?

The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.

For example, say you were analyzing Equifax sales data:

This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than \$500k.

### SQL Question 11: Joining Customers and Transactions Tables

Equifax is a global data analytics and technology company that provides information solutions. Considering this, let's look at a scenario where you have to analyze the customer database and join it with the transactions database.

Suppose you are asked to retrieve customer details and their corresponding total spend in the last year. Here are the sample data for the problem:

##### Example Input:
customer_idfirst_namelast_nameemail
101JohnDoejohn@email.com
102JaneSmithjane@email.com
103JamesBrownjames@email.com
105RobertJohnsonrobert@email.com
##### Example Input:
transaction_idcustomer_idtransaction_dateamount
10011012022-10-05 00:00:0035
10021012021-10-10 00:00:0045
10031032021-11-20 00:00:0055
10041042022-09-05 00:00:0065
10051052022-08-15 00:00:0075

Your task is to join the two tables on 'customer_id' and sum up all the transaction amounts for each customer within the last year. Assume that the current date is 5th October, 2022.

Your PostgreSQL query to achieve this would look like:

This query joins the 'customers' and 'transactions' tables on the 'customer_id' field. It limits the transactions considered to those within the last year, and it sums the amount for all the transactions of each customer. Finally, it returns the customer details along with their total spend in the last year.

Since joins come up frequently during SQL interviews, take a stab at this interactive Snapchat SQL Interview question using JOINS:

### Equifax SQL Interview Tips

The best way to prepare for a Equifax SQL interview is to practice, practice, practice. In addition to solving the above Equifax SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Google, Facebook, Microsoft and Amazon.

Each DataLemur SQL question has hints to guide you, detailed solutions and best of all, there is an interactive coding environment so you can instantly run your SQL query answer and have it executed.

To prep for the Equifax SQL interview you can also be helpful to solve interview questions from other financial services companies like:

Find out how Equifax is using AI to unlock new insights and opportunities in the world of credit and finance!

In case your SQL coding skills are weak, don't worry about going right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.

This tutorial covers topics including CASE/WHEN/ELSE statements and window functions like RANK() and ROW_NUMBER() – both of these come up frequently during SQL job interviews at Equifax.

### Equifax Data Science Interview Tips

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

Beyond writing SQL queries, the other types of problems to prepare for the Equifax Data Science Interview include:

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

To prepare for the Equifax Data Science interview make sure you have a firm understanding of the company's values and mission – this will be key to acing the behavioral interview. For the technical Data Science interviews, prepare by reading Ace the Data Science Interview. The book's got: