9 Navient SQL Interview Questions (Updated 2024)
Navient employees write SQL queries often for analyzing student loan data to assist in financial decision making. Because of this, Navient typically asks SQL coding questions during interviews for Data Science and Data Engineering positions.
To help prep you for the Navient SQL interview, we'll cover 9 Navient SQL interview questions can you solve them?
9 Navient SQL Interview Questions
SQL Question 1: Average Student Loan Balance Across Different Education Levels
Navient is a corporation that services student loans. Let's suppose that we're interested in analyzing the average student loan balance at different education levels on a monthly basis. The Navient database has a 'loans' table that contains loan information, including a unique loan_id, the loan_amount, the loan_issue_date, and the education_level of each borrower.
Example Input:
| loan_id | borrower_id | loan_issue_date | loan_amount | education_level |
|---|---|---|---|---|
| 101 | 222 | 04/03/2019 | 15000 | Undergraduate |
| 102 | 333 | 04/30/2019 | 32000 | Graduate |
| 103 | 444 | 05/12/2019 | 8000 | Undergraduate |
| 104 | 555 | 05/20/2019 | 45000 | Graduate |
| 105 | 666 | 06/15/2019 | 20000 | Graduate |
The question is: Write a SQL query to calculate the average loan amount for each education level month by month.
Answer:
In this query, we use the function to truncate the loan_issue_date to the nearest month, and then we partition the table by the truncated date and education level before calculating the average loan amount. Finally, the result is sorted by month and education level.
Please note, in the actual interview SQL problems might be more complicated depending on the structure of the available dataset. The main point is to showcase your skill in using window functions and understanding the business context.
To practice a related window function SQL problem on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:
Read about Navient's story and see how they became the business they are today.
SQL Question 2: Second Highest Salary
Imagine you had a table of Navient employee salary data. Write a SQL query to find the 2nd highest salary among all employees.
Navient Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Code your solution to this problem and run your code right in the browser:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: What does the clause do vs. the 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 Navient 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 4: Filter Student Loan Accounts Based on Status and Outstanding Balance
You are asked to generate a report from the table, including only the records where the customers are 'active', never defaulted on their loan, and have an outstanding balance greater than $5000.
The table has the following fields:
Example Input:
| loan_id | customer_id | status | defaulted | outstanding_balance |
|---|---|---|---|---|
| 1245 | 346 | active | no | 7100.00 |
| 6872 | 789 | inactive | no | 3000.50 |
| 2851 | 123 | active | yes | 9500.00 |
| 3786 | 456 | active | no | 5250.00 |
| 4829 | 789 | closed | no | 0.00 |
Answer:
Here's the SQL query you'd run:
This query retrieves all records from the table where the status is 'active', the customer never defaulted ('no' in the defaulted column), and the outstanding balance is greater than $5000. The asks for all fields from the filtered records.
Example Output:
| loan_id | customer_id | status | defaulted | outstanding_balance |
|---|---|---|---|---|
| 1245 | 346 | active | no | 7100.00 |
| 3786 | 456 | active | no | 5250.00 |
SQL Question 5: 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. For example, let's analyze Navient's Google Ads campaigns data:
:
+------------+------------+------------+------------+ | ad_id | campaign_id| keyword | click_count| +------------+------------+------------+------------+ | 1 | 201 | Navient reviews | 120 | | 2 | 202 | Navient pricing | 150 | | 3 | 101 | buy Navient | 65 | | 4 | 101 | Navient alternatives | 135 | +------------+------------+------------+------------+
is a foreign key that connects to the of the corresponding Google Ads campaign. This establishes a relationship between the ads and their campaigns, enabling easy querying to find which ads belong to a specific campaign or which campaigns a specific ad belongs to.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to link each ad to its ad group and the Google Ads account that the campaigns belong to, respectively.
SQL Question 6: Calculate Click-Through Conversion Rates for Navient's Digital Products
Navient is a company that provides education loan management and business processing solutions. Let's say a typical problem we may face involves calculating the conversion rates of their loan product pages. Specifically, let's talk about their loan product pages - the number of times a specific loan product page was viewed, the number of times a specific loan product was added to the cart, and the calculated ratio (add to cart / views).
Let's assume we have two tables as follows:
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 7814 | 134 | 2022-06-08 00:00:00 | 60001 |
| 2635 | 265 | 2022-06-10 00:00:00 | 76025 |
| 3536 | 362 | 2022-06-14 00:00:00 | 60001 |
| 2745 | 192 | 2022-07-05 00:00:00 | 76025 |
Example Input:
| action_id | user_id | action_date | product_id |
|---|---|---|---|
| 5926 | 134 | 2022-06-12 00:00:00 | 60001 |
| 7886 | 265 | 2022-06-14 00:00:00 | 76025 |
| 5293 | 735 | 2022-07-05 00:00:00 | 76025 |
Given the above tables, we want to calculate the conversion rate for each product for each month that shows the proportion of page views that resulted in addition to the cart in a specific month.
Answer:
The SQL query may look like this:
This SQL query groups the data by the month and the product. It then calculates and returns the ratio of distinct actions (add to cart) to the distinct views for each product per month. This gives us the conversion rate of page views which resulted in adding the product to the cart for each month. The ensures that we still count page views even if they didn't result in adding to the cart.
To practice a related SQL problem on DataLemur's free interactive coding environment, attempt this Meta SQL interview question:
SQL Question 7: When considering database normalization, how do 1NF, 2NF, and 3NF differ from one another?
There are several normal forms that define the rules for normalizing a database:
A database is in first normal form (1NF) if it meets the following criteria:
- Each column in a table contains a single value (no lists or containers of data)
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each row in the table is unique
A database is in second normal form (2NF) if it meets the following criteria:
- It is in first normal form.
- All non-key attributes in a table are fully dependent on the primary key.
Said another way, to achieve 2NF, besides following all the rules from 1NF all the columns in a given table should be dependent only on that table's primary key.
A database is in third normal form (3NF) if it meets the following criteria:
- It is in second normal form.
- There are no transitive dependencies in the table.
A transitive dependency means that a piece of data in one column is derived from another column. For example, it wouldn't make sense to keep a column called "user's age" and "user's birthdate" (because age can be derived from birthdate.
While there's also a 4th and 5th normal form, it's too pedantic and hence out-of-scope to know for the Navient SQL interview.
SQL Question 8: Average Loan Amount Per Loan Type Per Year
Navient is a company that services and collects on student loans. For this question, assume you are given a 'loans' table. The table tracks the amount of each loan (in dollars), the type of the loan (e.g., undergraduate, graduate, parent), and the date the loan was disbursed.
The question is to write a query that calculates the average amount of each type of loan, per year. Assume all loans are disbursed in the same year they are granted.
Example Input Table:
| loan_id | disburse_date | loan_type | amount |
|---|---|---|---|
| 001 | 01/15/2021 | undergraduate | 5000 |
| 002 | 03/10/2021 | graduate | 10000 |
| 003 | 04/20/2021 | undergraduate | 6000 |
| 004 | 07/15/2021 | parent | 15000 |
| 005 | 08/20/2022 | graduate | 12000 |
| 006 | 09/18/2022 | undergraduate | 5500 |
Example Output:
| year | loan_type | avg_amount |
|---|---|---|
| 2021 | undergraduate | 5500 |
| 2021 | graduate | 10000 |
| 2021 | parent | 15000 |
| 2022 | undergraduate | 5500 |
| 2022 | graduate | 12000 |
Answer:
This SQL query uses the function to group the loans according to the year they were disbursed and their type. The function is then used on the column to find the average amount for each grouping. The function is used to get the year from the timestamp. As a result, for each loan type per year, we get the average loan amount.
SQL Question 9: Filtering and Searching Customer Records
Navient is a company that provides students loan and other financial services. To manage their client base, they have a database table that stores customer details.
Your task is to write an SQL query that will filter all customer records whose first names start with 'J' and their loan amount is above $50,000.
Here's an example of how the table might look like:
Example Input:
Here's an example of what the result of your query might look like:
Example Output:
Answer:
To solve this problem, you can make use of the SQL keyword. The pattern to match names starting with 'J' is , and to match loan amounts above $50,000 you can use the condition .
Here's your SQL query:
This query will return all the rows from the table where the customer's first name starts with 'J' and their loan amount is above $50,000.
Navient SQL Interview Tips
The best way to prepare for a Navient SQL interview is to practice, practice, practice.
In addition to solving the above Navient SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG and tech startups.
Each problem on DataLemur has multiple hints, detailed solutions and most importantly, there's an online SQL code editor so you can instantly run your SQL query and have it checked.
To prep for the Navient SQL interview it is also useful to solve SQL questions from other mortgage & loan companies like:
However, if your SQL coding skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers SQL topics like AND/OR/NOT and handling NULLs in SQL – both of these come up often during SQL job interviews at Navient.
Navient Data Science Interview Tips
What Do Navient Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems to practice for the Navient Data Science Interview include:
- Experimental Design & AB Testing Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Navient Data Science Interviews?
I'm sorta biased, but I think the best way to prepare for Navient Data Science interviews is to read the book Ace the Data Science Interview.
The book covers 201 interview questions sourced from FAANG & startups. It also has a refresher covering SQL, Product-Sense & ML. And finally it's vouched for by the data community, which is why it's got over 1000+ 5-star reviews on Amazon.