# 9 USAA SQL Interview Questions (Updated 2024)

Updated on

August 29, 2024

USAA employees write SQL queries for extracting and analyzing customer data, including financial transaction history and credit scores, to optimize financial products. It is also used for handling large datasets for fraud detection and prevention by identifying suspicious transactions, which is why USAA includes SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.

So, to help you prep, we've collected 9 USAA SQL interview questions – can you solve them?

## 9 USAA SQL Interview Questions

### SQL Question 1: Analyzing Customer Product Reviews

USAA is interested in understanding the trends of products based on the stars given by customers in their reviews. To achieve this, they would like to analyze the average stars given to each product monthly. Write a SQL query to create a table that shows the month, product, and average stars for each product per month.

Please note that format is 'MM/DD/YYYY HH24:MI:SS' and you are required to use a SQL window function in your solution.

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522
##### Example Output:
monthproductavg_stars
6500013.50
6698524.00
7698522.50

This solution creates a window that groups data by and the month extracted from . It then calculates the average stars for each group (i.e., for each product per month). The function is applied to compute the average within each window partition. This provides USAA with a monthly understanding of product performance as judged by their customers.

p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur

### SQL Question 2: Top Department Salaries

Given a table of USAA employee salaries, write a SQL query to find the top 3 highest paid employees in each department.

#### USAA Example Input:

employee_idnamesalarydepartment_id
1Emma Thompson38001
2Daniel Rodriguez22301
3Olivia Smith20001
4Noah Johnson68002
5Sophia Martinez17501
8William Davis68002
10James Anderson40001

#### Example Input:

department_iddepartment_name
1Data Analytics
2Data Science

#### Example Output:

department_namenamesalary
Data AnalyticsJames Anderson4000
Data AnalyticsEmma Thompson3800
Data AnalyticsDaniel Rodriguez2230
Data ScienceNoah Johnson6800
Data ScienceWilliam Davis6800

Try this interview question interactively on DataLemur:

We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.

If the code above is confusing, you can find a detailed solution with hints here: Top 3 Department Salaries.

### SQL Question 3: Can you explain the distinction between a correlated and a non-correlated sub-query?

While a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.

An example correlated sub-query:

This correlated subquery retrieves the names and salaries of USAA employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).

An example non-correlated sub-query:

This non-correlated subquery retrieves the names and salaries of USAA employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the USAA employees table.

### SQL Question 4: USAA Insurance Claims Management

USAA is widely known for its auto insurance services. As part of their services, they deal with numerous insurance claims. Design a database system to handle the management of auto insurance claims. The database needs to track details about policies, customers, and vehicles involved, as well as specifics of each claim - like date of incident, description, status of claim, and the amount. Based on this initial design, how would you retrieve all claims of a specific customer by their customer_id?

##### Example Input:
customer_idfirst_namelast_nameemail
101JohnDoejohndoe@gmail.com
102JaneSmithjanesmith@yahoo.com
103MikeAndersonmikeanderson@hotmail.com
##### Example Input:
policy_idcustomer_idstart_dateend_date
20110101/01/2021 00:00:0031/12/2021 00:00:00
20210201/02/2022 00:00:0031/01/2023 00:00:00
20310301/03/2022 00:00:0002/03/2023 00:00:00
##### Example Input:
vehicle_idpolicy_idmakemodelyear
301201ToyotaCorolla2020
302202FordEscape2019
303203HondaCivic2018
##### Example Input:
policy_idclaim_iddate_of_incidentdescriptionclaim_statusclaim_amount
20140101/06/2021 00:00:00Minor collisionClosed\$2000
20240201/07/2022 00:00:00Windshield damageOpen\$500
20340301/08/2022 18:30:00Major collisionOpen\$10000

This query would return all insurance claims for a specific customer. It joins the four tables on the appropriate foreign key and filters the results based on a customer_id. Replace with the specific customer_id you want to filter by.

### SQL Question 5: Consider unique indexes, and non-unique indexes. What are some similarities and differences?

Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.

Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.

Unique Indexes are blazing fast. Non unique indexes can improve query performance, but they are often slower because of their non-unique nature.

### SQL Question 6: Filtering USAA Customers Based on their Status and Recent Transactions

As a part of USAA's data analysis team, your task is to retrieve the record details of all the customers who are 'Active' status and have completed a transaction greater than \$5000 within the last 30 days. Also, you need to provide the count of such customers.

#### Example Input:

customer_idfirst_namelast_namestatus
001JaneDoeActive
002JohnDoeActive
003DavidSmithNot-Active
004EmilyJonesActive

#### Example Input:

transaction_idcustomer_idtransaction_dateamount
100100104/05/2023\$5500
100200203/01/2023\$2000
100300304/10/2023\$7000
100400402/15/2023\$8000

#### Example Output:

customer_idfirst_namelast_nameamount
001JaneDoe\$5500

#### Count of customers meeting conditions: 1

In the first query, we are joining the 'customer' and 'transaction_details' tables using the 'customer_id' field which is present in both the tables. We then filter the records based on the conditions specified - customers should be 'Active', transaction date should be within the last 30 days and the transaction amount should be greater than \$5000.

The second query returns the count of such customers. It has the same conditions as the first query but instead of returning the customer details, it returns the count of such records.

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

is used to combine the output of multiple statements into one big result!

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at USAA, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use in the following way:

### SQL Question 8: Analyzing Click-through-Rates for USAA

USAA, a company providing a range of financial products and services, uses digital advertising to attract potential customers. They track user interactions from viewing an ad to eventually signing up for a service.

You are given two tables: and . The table records every time a user clicks on one of USAA's ads. The table records every time a user signs up for USAA's services after clicking an ad.

Determine the click-through conversion rate, which is the number of sign-ups divided by the number of clicks.

##### Example Input:
click_iduser_idclick_date
122408/01/2022 00:00:00
255508/02/2022 00:00:00
378908/02/2022 00:00:00
4101108/03/2022 00:00:00
5131008/04/2022 00:00:00
##### Example Input:
signup_iduser_idsignup_date
122408/02/2022 00:00:00
255508/03/2022 00:00:00
378608/06/2022 00:00:00

This SQL query computes the click-through conversion rate by dividing the distinct count of user IDs in the table by the distinct count of user IDs in the table. The ensures that all clicks are included in the calculation, regardless whether or not they resulted in a signup. This provides the proportion of users who clicked on an ad and subsequently signed up for a service, giving an indication of the effectiveness of the advertising campaign.

To solve a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's online SQL coding environment:

### SQL Question 9: Average Insurance Claim

USAA provides insurance for many vehicles. They typically track the insurance claims made by users for each vehicle type. For this question, assume the following scenario: you have a table which has columns for , , , (categories like 'Car', 'Boat', 'Motorcycle', etc.), and .

Write a SQL query that finds the average claim amount for each vehicle type for the year 2021. List the vehicle types in descending order of the average claim amount.

##### Example Input:
claim_iduser_idclaim_datevehicle_typeclaim_amount
110202/15/2021Car500
210504/20/2021Boat1000
310805/10/2021Motorcycle600
411208/20/2021Car700
511509/15/2021Boat1500

This PostgreSQL query first filters the claims to include only those made in the year 2021. It then groups the claims by vehicle type and calculates the average claim amount for each group. The results are then ordered in descending order of the average claim amount, so the vehicle type with the highest average claim amount will be listed first.

##### Example Output:
vehicle_typeavg_claim_amount
Boat1250
Motorcycle600
Car600

### Preparing For The USAA SQL Interview

The key to acing a USAA SQL interview is to practice, practice, and then practice some more! In addition to solving the above USAA SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.

Each exercise has multiple hints, step-by-step solutions and best of all, there is an interactive coding environment so you can right online code up your query and have it checked.

To prep for the USAA SQL interview it is also a great idea to solve SQL problems from other insurance companies like:

See how USAA's automated investment platform can help you make informed investment decisions with confidence!

But if your SQL skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL tutorial.

This tutorial covers SQL topics like SQL joins with practice exercises and CTEs – both of these show up frequently during USAA interviews.

### USAA Data Science Interview Tips

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

Beyond writing SQL queries, the other types of problems covered in the USAA Data Science Interview are:

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

To prepare for the USAA Data Science interview make sure you have a strong understanding of the company's values and company principles – this will be clutch for acing the behavioral interview. For technical interviews get ready by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from FAANG tech companies
• A Refresher on SQL, Product-Sense & ML
• Great Reviews (1000+ reviews, 4.5-star rating)