8 Upstart SQL Interview Questions (Updated 2024)
At Upstart, SQL is often used for analyzing loan databases for risk assessment, and for customer analytics. That's the reason behind why Upstart almost always asks SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
As such, to help you prepare for the Upstart SQL interview, this blog covers 8 Upstart SQL interview questions – able to solve them?
8 Upstart SQL Interview Questions
SQL Question 1: Calculate Monthly Average Loan Amount
Upstart, being an online lending platform, wants to understand how their average loan amount changes over time. This could help them identify any trends or seasonality in their loans business to make better business decisions. They specifically want to understand the monthly average loan amount for each unique loan purpose over the last year.
You're provided with a table with the following schema:
Example Input:
| loan_id | loan_purpose | loan_amount | loan_date |
|---|---|---|---|
| 2345 | Car | 18000 | 2019-03-23 |
| 3782 | Investment | 15000 | 2019-03-23 |
| 8542 | Education | 25000 | 2019-04-14 |
| 9529 | Investment | 20000 | 2019-04-14 |
| 1654 | Car | 12000 | 2019-04-14 |
Write a SQL query to get the monthly average loan amount for each unique loan purpose over the last year. The output should include the year, the month, the loan purpose, and the relevant monthly average loan amount.
The output of the SQL query should look like this:
Example Output:
| yr | mth | purpose | avg_loan_amount |
|---|---|---|---|
| 2019 | 03 | Car | 18000.00 |
| 2019 | 03 | Investment | 15000.00 |
| 2019 | 04 | Car | 12000.00 |
| 2019 | 04 | Investment | 20000.00 |
| 2019 | 04 | Education | 25000.00 |
Answer:
This SQL query uses the function to break down the into and . The is then used as our window function to compute the average for each combination of , , and . The clause limits our data to the last year. Grouping and ordering is done by , , and to break down the average loan amounts as per the requirements.
For more window function practice, try this Uber SQL problem on DataLemur's online SQL code editor:
SQL Question 2: Loan Distribution Analysis
Upstart is a leading AI lending platform that partners with banks to improve access to affordable credit. A part of their business is to understand how their loans are distributed among different customers in terms of loan size, occupation. They have two tables and .
table:
This table contains information about each borrower who took a loan with upstart.
| borrower_id | age | occupation |
|---|---|---|
| 101 | 25 | Engineer |
| 102 | 32 | Teacher |
| 103 | 28 | Doctor |
| 104 | 35 | Engineer |
| 105 | 30 | Teacher |
table:
This table contains information about the loans taken by the borrowers.
| loan_id | borrower_id | loan_size |
|---|---|---|
| 201 | 101 | 10000 |
| 202 | 102 | 15000 |
| 203 | 103 | 8000 |
| 204 | 104 | 20000 |
| 205 | 105 | 5000 |
The business problem: Upstart wants to know the average loan size by occupation of borrowers and their age. They want to check if occupation and age are factors that determine the size of the loan a customer takes.
Answer:
We can solve this problem by joining the two tables on 'borrower_id' and then grouping by 'occupation' and 'age' to calculate the average loan size.
This query will return a table with each row representing a unique combination of occupation and age, and a column representing the average loan size for that combination. This will allow Upstart to analyze whether certain occupations or age groups tend to take out larger loans.
SQL Question 3: What sets a cross join apart from a natural join?
Imagine you are organizing a party and have two database tables: one table of people you want to invite and another list of food items you want to serve.
A cross join would be like inviting every person on your list to the party and serving them every food item on the menu, regardless of whether they like the food or not. So, if you had 10 people on your invite list and 5 food items on the menu, you would generate all 50 different combinations of people and food (10 x 5 = 50).
On the other hand, a natural join would be like inviting only the people who like the food items on the menu (based on doing a inner/left/right/outer JOIN on a common key like ).
SQL Question 4: Calculating Product View to Cart Conversion Rate
Upstart wants to understand the click-through-rate from viewing a product to adding a product to the cart. They have two tables:
- table, which records every time a user views a product.
- table, which records every time a user adds a product to their cart.
Calculate the conversion rate from viewing a product to adding a product to cart, for each product.
Example Input:
| view_id | user_id | product_id | time_stamp |
|---|---|---|---|
| 8171 | 123 | 10001 | 06/08/2022 00:00:00 |
| 5802 | 265 | 20002 | 06/10/2022 00:00:00 |
| 8293 | 362 | 10001 | 06/18/2022 00:00:00 |
| 9352 | 192 | 30003 | 07/26/2022 00:00:00 |
| 9517 | 981 | 20002 | 07/05/2022 00:00:00 |
Example Input:
| add_id | user_id | product_id | time_stamp |
|---|---|---|---|
| 4171 | 123 | 10001 | 06/08/2022 00:01:00 |
| 2802 | 265 | 20002 | 06/10/2022 00:01:00 |
| 6293 | 362 | 10001 | 06/18/2022 00:01:00 |
| 8352 | 981 | 20002 | 07/05/2022 00:01:00 |
Answer:
This query counts the distinct users who viewed and added each product to their cart. We make a from to on and to see which product views led to adds. The conversion rate is then calculated by dividing by . We use function to avoid division by zero.
To solve a similar SQL problem on DataLemur's free interactive SQL code editor, try this SQL interview question asked by Facebook:
SQL Question 5: In database normalization, what's the distinction between 1NF, 2NF, and 3NF?
Normal forms are guidelines that are used to help design a relational database in a way that minimizes redundancy and ensures the integrity of the data. The 3 most commonly use normal forms are the 1st, 2nd, and 3rd normal forms. Here's a brief explanation of each:
-
1st Normal Form (1NF) is all about keeping it simple - each column should only have one value and there should be no repeating groups of data.
-
2nd Normal Form (2NF) is about organization - your database should already be in 1NF and all the non-key columns should depend on the primary key. This means that each non-key column should be completely dependent on the entire primary key, not just part of it.
-
3rd Normal Form (3NF) is about independence - if your database is already in 2NF, then all the non-key columns should not depend on each other. They should be self-sufficient and not rely on other non-key columns.
SQL Question 6: Calculate Average Loan Amount by State
Upstart provides personal loans to customers. The table contains information about each loan issued by Upstart, including the loan ID, the customer ID, the loan amount, and the date the loan was issued. The table contains data about each customer, including their state of residence.
Given the and tables, write a SQL query to calculate the average loan amount by state for the past year.
Here are the sample tables:
Example Input:
| loan_id | customer_id | loan_amount | loan_date |
|---|---|---|---|
| 6171 | 123 | 7000 | 09/05/2021 |
| 7802 | 265 | 5000 | 11/20/2021 |
| 5293 | 362 | 9000 | 12/10/2021 |
| 6352 | 192 | 8000 | 04/25/2021 |
| 4517 | 981 | 6000 | 07/15/2021 |
Example Input:
| customer_id | state |
|---|---|
| 123 | NY |
| 265 | CA |
| 362 | TX |
| 192 | NY |
| 981 | CA |
Answer:
This query joins the and tables on , and then selects the and after grouping the data by . The clause filters the data to include only loans issued in the past year.
Because join questions come up routinely during SQL interviews, take a stab at this Spotify JOIN SQL question:
SQL Question 7: What is denormalization, and in what situations might it be a useful?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
For example, in a database that stores Upstart sales analytics data, you might have separate tables for "customers," "orders," and "products," with foreign key constraints linking the tables together. This helps to ensure the integrity of the data and reduces redundancy, but it can also make queries that involve multiple tables more complex and slower to execute.
By de-normalizing the database, you can combine some of the data from separate tables into a single table, which can reduce the number of joins that are required to retrieve the data you need. This can speed up queries and improve the performance of your database.
However, it's important to carefully consider the trade-offs of de-normalization before making any changes to your database. De-normalization can make it more difficult to maintain the integrity and reliability of your data, and can also increase the risk of data redundancy. It's generally best to use de-normalization as a performance optimization technique only when necessary, and to carefully evaluate the benefits and drawbacks in the context of your specific database and workload.
SQL Question 8: Average Loan Amount by Month
As an analyst at Upstart, a company providing personal loans online, you have been tasked to monitor the average loan amount provided per month. Given a table 'loans', create a SQL query to find the average loan amount for each month.
Example Input:
| loan_id | user_id | loan_date | loan_amount |
|---|---|---|---|
| 6789 | 345 | 01/22/2022 00:00:00 | 15000 |
| 7980 | 256 | 03/10/2022 00:00:00 | 22000 |
| 3284 | 563 | 03/25/2022 00:00:00 | 18000 |
| 5382 | 785 | 07/19/2022 00:00:00 | 30000 |
| 1478 | 143 | 10/09/2022 00:00:00 | 24000 |
Example Output:
| month | average_loan_amount |
|---|---|
| 1 | 15000.00 |
| 3 | 20000.00 |
| 7 | 30000.00 |
| 10 | 24000.00 |
Answer:
In this query, we use the PostgreSQL specific function to get the month from loan_date. The function is used to calculate average loan amount for each month. We then month to find average loan amount for each distinct month. Finally, we month to provide a chronologically ordered output.
How To Prepare for the Upstart SQL Interview
The best way to prepare for a Upstart SQL interview is to practice, practice, practice.
In addition to solving the earlier Upstart SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each DataLemur SQL question has hints to guide you, full answers and crucially, there is an interactive SQL code editor so you can right online code up your SQL query and have it executed.
To prep for the Upstart SQL interview it is also wise to practice SQL questions from other tech companies like:
However, if your SQL query skills are weak, don't worry about jumping right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers things like Union vs. UNION ALL and rank window functions – both of which pop up often in SQL interviews at Upstart.
Upstart Data Science Interview Tips
What Do Upstart Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems covered in the Upstart Data Science Interview are:
- Probability & Statistics Questions
- Python or R Programming Questions
- Open-Ended Data Case Studies
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Upstart Data Science Interviews?
To prepare for Upstart Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
- a crash course on Product Analytics, SQL & ML
- over 900+ 5-star reviews on Amazon