10 Affirm SQL Interview Questions (Updated 2024)
At Affirm, SQL is used to retrieve data on loan applicant creditworthiness, payment history, and credit scores to build predictive models for loan approval and interest rate determination. It is also used to create data-driven marketing strategies by analyzing customer demographics, purchase behavior, and marketing campaign effectiveness, which is why Affirm asks SQL problemsduring interviews for Data Science and Data Engineering positions.
So, to help you practice, we've curated 10 Affirm SQL interview questions – able to answer them all?
10 Affirm SQL Interview Questions
SQL Question 1: Identifying Power Users in Affirm's Customer Database
In the customer database of Affirm, write an SQL query to identify the "power users". These users are defined as those who have made more than 10 transactions in the past month. You'll need to provide their user_id, total amount financed, and the count of transactions.
Example Input:
| transaction_id | user_id | transaction_date | financed_amount |
|---|---|---|---|
| 1001 | 500 | 08/10/2022 00:00:00 | 2000 |
| 1011 | 245 | 08/10/2022 00:00:00 | 1500 |
| 1022 | 500 | 08/18/2022 00:00:00 | 1200 |
| 1045 | 245 | 08/19/2022 00:00:00 | 5000 |
| 1057 | 123 | 08/25/2022 00:00:00 | 800 |
| 1064 | 500 | 09/01/2022 00:00:00 | 600 |
| 1081 | 245 | 09/02/2022 00:00:00 | 1000 |
| 1088 | 123 | 09/06/2022 00:00:00 | 700 |
| 1096 | 245 | 09/09/2022 00:00:00 | 1250 |
| 1100 | 123 | 09/10/2022 00:00:00 | 600 |
| 1108 | 245 | 09/12/2022 00:00:00 | 700 |
| 1116 | 500 | 09/13/2022 00:00:00 | 900 |
Answer:
This query involves selecting from the transactions table the user_id, the sum of the financed amounts (as total_financed), and the count of transactions. We are interested only in data within the last month, so we apply the WHERE condition on transaction_date to only select rows where the transaction_date falls in the previous month. The data is grouped by the user_id with a HAVING clause to only retain rows where the transaction count is greater than 10.
To solve a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: Second Highest Salary
Assume there was a table of Affirm employee salary data. Write a SQL query to find the 2nd highest salary among all employees.
Affirm Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Check your SQL query for this interview question and run your code right in DataLemur's online SQL environment:
Answer:
You can find a detailed solution with hints here: 2nd Highest Salary.
SQL Question 3: Name the different types of joins in SQL. What does each one do?
In SQL, a join retrieves rows from multiple tables and combines them into a single result set.
Four JOIN types can be found in SQL. For an example of each one, suppose you had a table of Affirm orders and Affirm customers.
-
INNER JOIN: Rows from both tables are retrieved when there is a match in the shared key or keys. An between the Orders and Customers tables would return only rows where the in the Orders table matches the in the Customers table.
-
LEFT JOIN: A retrieves all rows from the left table (in this case, the Orders table) and any matching rows from the right table (the Customers table). If there is no match in the right table, NULL values will be returned for the right table's columns.
-
RIGHT JOIN: A combines all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be displayed for the left table's columns.
-
FULL OUTER JOIN: A combines all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be displayed for the columns of the non-matching table.
SQL Question 4: Calculate the Monthly Average Loan Amount
As an interviewee at Affirm, you know that Affirm provides loans to customers, hence a dataset consisting of loan information would be relevant.
The interview question requires analyzing a hypothetical dataset with several columns: , , (the date the loan was issued), and .
Suppose we want to calculate the monthly average loan amount. The expected output should have a monthly breakdown (year and month), along with the calculated average loan amount for each month.
Example Input:
| loan_id | customer_id | loan_issue_date | loan_amount |
|---|---|---|---|
| 8739 | 996 | 2022-06-01 | 1200 |
| 9457 | 925 | 2022-06-10 | 2500 |
| 4628 | 734 | 2022-07-10 | 1500 |
| 8400 | 362 | 2022-07-22 | 2200 |
Example Output:
| year_month | avg_loan_amount |
|---|---|
| 2022-06 | 1850 |
| 2022-07 | 1850 |
Answer:
This query truncates the to month, then it calculates and groups the average loan amount by year and month. With this SQL window function, we are able to analyze monthly trends in the average loan amount issued by Affirm.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What is a cross-join?
A cross-join, also known as a cartesian join, is a type of join that returns the cross-product of two tables. In a cross-join, each row from the first table is paired with every row from the second table, resulting in a new table that has a row for each possible combination of rows from the two input tables.
For example, say you worked on the Marketing Analytics team at Affirm, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Affirm. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows!
SQL Question 6: Filter Affirm's Customer Records
As an analytics engineer at Affirm, you have been tasked with finding out the number of customers that took a loan with Affirm in the year 2022, but are yet to pay their debt as of 2023. These customers are from California, have a credit score of at least 650, and have taken a loan amount of over $10,000. Create a query to filter these customers based on these conditions.
Suppose you have the following customer and loan tables.
Example Input:
| customer_id | name | state | credit_score |
|---|---|---|---|
| 123 | John Doe | California | 670 |
| 456 | Jane Smith | New York | 700 |
| 789 | Sarah Johnson | California | 660 |
| 321 | Michael Brown | California | 640 |
| 654 | Emily Davis | Texas | 650 |
Example Input:
| loan_id | customer_id | amount | loan_date | repaid_date |
|---|---|---|---|---|
| 6171 | 123 | $15000 | 01/08/2022 | null |
| 7802 | 456 | $5000 | 03/10/2022 | 12/10/2022 |
| 5293 | 789 | $11000 | 06/18/2022 | null |
| 6352 | 321 | $9000 | 07/26/2022 | 01/15/2023 |
| 4517 | 654 | $20000 | 1207/2022 | 05/05/2023 |
Answer:
The query joins the customer and loans tables on the field. After joining, it then filters on the conditions specified. The operator is used to ensure all conditions are met. The function is used to get the year of the loan, and the checks if the debt has been repaid or not.
SQL Question 7: What does do?
The function can take in multiple paramaters, and returns the first input paramater that is not null. If all arguments are null, the COALESCE function will return null too.
Suppose you have a table of Affirm salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.
| sales_person | closed_deals |
|---|---|
| Jason Wright | NULL |
| Drew Jackson | 3 |
| Chris Ho | NULL |
| Adam Cohen | 2 |
| Samantha Perez | 4 |
To change these NULLs to zeros, you can use the function in the following way:
This would result in the following data:
| sales_person | closed_deals |
|---|---|
| Jason Wright | 0 |
| Drew Jackson | 3 |
| Chris Ho | 0 |
| Adam Cohen | 2 |
| Samantha Perez | 4 |
SQL Question 8: Average Loan Amount per User
Affirm facilitates installment loans to customers at the point of sale. So, a relevant question to Affirm might be to find the average loan amount per user.
Assume we have a table called that contains data about each loan extended by Affirm. This table has the following fields:
- : The unique identifier for the loan.
- : The unique identifier for the customer who took out the loan.
- : The date the loan was issued.
- : The amount of the loan.
Example Input:
| loan_id | user_id | loan_date | loan_amount |
|---|---|---|---|
| 1 | 101 | 07/07/2022 | 400 |
| 2 | 102 | 07/07/2022 | 500 |
| 3 | 101 | 07/08/2022 | 600 |
| 4 | 103 | 07/09/2022 | 700 |
| 5 | 102 | 07/10/2022 | 800 |
We want to find the average loan amount for each user. For example, given the above data, the average loan amount for user 101 is .
Example Output:
| user_id | avg_loan_amount |
|---|---|
| 101 | 500 |
| 102 | 650 |
| 103 | 700 |
Answer:
This query calculates the average loan amount for each user. It uses the aggregate function to find the average for each . The clause groups the data by , so the aggregation is performed separately for each user. The result is a list of users and their average loan amount.
To practice a very similar question try this interactive Twitter Histogram of Tweets Question which is similar for aggregate data per user or this Stripe Repeated Payments Question which is similar for identifying transactions per user.
SQL Question 9: Click Through Conversion Rates at Affirm
Assume you are data analyst at Affirm, a company that offers installment loans to consumers at the point of sale. A common metric for them is the click-through conversion rates of there loan product from viewing the product details to proceeding to the loan application. The click-through rate is calculated as the number of unique users who proceed to the loan application after viewing the product details divided by total number of unique users who viewed the product details.
For this week, they want to know the click-through conversion rates of there loan products.
Example Input
| view_id | user_id | timestamp | product_id |
|---|---|---|---|
| 1371 | 101 | 06/18/2022 00:00:00 | 80001 |
| 1620 | 105 | 06/10/2022 00:00:00 | 80001 |
| 1660 | 103 | 06/18/2022 00:00:00 | 80002 |
| 1057 | 101 | 06/26/2022 00:00:00 | 80001 |
| 1218 | 107 | 07/05/2022 00:00:00 | 80002 |
Example Input
| application_id | user_id | timestamp | product_id |
|---|---|---|---|
| 2840 | 103 | 06/18/2022 00:00:00 | 80002 |
| 3380 | 105 | 06/10/2022 00:00:00 | 80001 |
| 3229 | 107 | 07/05/2022 00:00:00 | 80002 |
| 3171 | 109 | 07/05/2022 00:00:00 | 80001 |
Answer:
This query starts by creating two separate common table expressions (CTEs), one for total unique product views and another for total unique loan applications. It then joins these two CTEs on product_id and calculates the click-through conversion rate for each product.
To practice a related SQL interview question on DataLemur's free online SQL code editor, try this Meta SQL interview question:
SQL Question 10: What do stored procedures do?
Stored procedures in SQL are like recipes in a cookbook. Just like a recipe tells you the ingredients and instructions for making a particular dish, a stored procedure tells the DBMS the logic/statements needed to perform a specific task. Just like you can use a recipe to make the same dish over and over again, you can use a stored procedure to repeat the same task multiple times with different input parameters (which is why stored procedures are so damn useful!).
Say you were a Data Analyst working on a HR analytics project. A common sub-task you might have to do is calculate the average salary for a given department at Affirm, which would be perfect for a stored procedure:
To call this stored procedure and find the average salary for the Data Science department you'd execute a query like this:
Affirm SQL Interview Tips
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. In addition to solving the above Affirm SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Google, Uber, and Microsoft.
Each SQL question has multiple hints, step-by-step solutions and most importantly, there is an online SQL code editor so you can right online code up your SQL query answer and have it checked.
To prep for the Affirm SQL interview it is also useful to solve SQL questions from other payments & loan companies like:
Dive into the world of financial technology with Affirm's latest news and discover how they're making waves in the industry!
However, if your SQL query skills are weak, forget about diving straight into solving questions – go learn SQL with this free SQL tutorial.
This tutorial covers SQL topics like CTEs and creating summary stats with GROUP BY – both of which show up routinely in Affirm SQL interviews.
Affirm Data Science Interview Tips
What Do Affirm Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems to prepare for the Affirm Data Science Interview include:
- Statistics and Probability Questions
- Coding Questions in Python or R
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Interview Questions
How To Prepare for Affirm Data Science Interviews?
To prepare for the Affirm Data Science interview make sure you have a firm understanding of the company's values and company principles – this will be important for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Google, Microsoft & tech startups
- A Refresher on Python, SQL & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)