# 10 Northwestern Mutual SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

Northwestern Mutual employees use SQL for analyzing financial data, including investment portfolio performance and market trends, for trend discovery, as well as managing databases, such as policyholder information and claims history, for efficient report generation. That is the reason why Northwestern Mutual usesSQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.

To help you practice for the Northwestern Mutual SQL interview, we'll cover 10 Northwestern Mutual SQL interview questions in this blog.

## 10 Northwestern Mutual SQL Interview Questions

### SQL Question 1: Calculate Monthly Average Premium for Each Insurance Type

Suppose you work for Northwestern Mutual and you are tasked with analyzing the insurance premiums data. You have to come up with a SQL query to calculate the average premium per type of insurance plan for each month. The dataset available to you is 'plans_premiums'.

##### Example Input:
1101123Health20002/18/2022 00:00:00
1202265Life15002/10/2022 00:00:00
1303362Motor25003/18/2022 00:00:00
1504192Health30002/26/2022 00:00:00
1705981Life35003/05/2022 00:00:00
##### Expected Output:
2Health250
2Life150
3Motor250
3Life350

This PostgreSQL query uses the GROUP BY clause to group the rows by month and insurance type. The AVG() function is used within the select statement to calculate the average premium. The EXTRACT() function is used to extract the month from the date field.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

### SQL Question 2: Top Three Salaries

Assume there was a table of Northwestern Mutual employee salary data. Write a SQL query to find the top three highest paid employees within each department.

#### Northwestern Mutual 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

Write a SQL query for this 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 solution above is tough, you can find a step-by-step solution with hints here: Top 3 Department Salaries.

### SQL Question 3: What is a cross-join, and when would you use one?

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 Northwestern Mutual, 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 Northwestern Mutual. 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 4: Financial Advisor and Client Database Design

Northwestern Mutual is a respected financial services company that makes use of Financial Advisors to facilitate customers' transactions and track activities. The company needs to match clients with financial advisors, track the interactions between them and record the clients' transactions details.

Design a database to capture this scenario and subsequently answer the following question using PostgreSQL:

"Retrieve the total amount of money each advisor has managed to invest for his/her clients in the year 2022."

Appropriate tables might look something like this:

101John Doe
102Jane Smith
103Bob Johnson
##### Example Input:
201Alice Brown101
202Charlie Davis102
203Eve Fox101
204David Gray103
##### Example Input:
transaction_idclient_idinvestment_amounttransaction_date
1001201500001/10/2022
1002202700002/20/2022
1003201300003/15/2022
1004203400004/18/2022
1005204600005/22/2022

This query would return the name of each advisor and the sum of the investment amounts for all transactions they have handled in the year 2022.

While this database design is simple, it's powerful in solving the problem. Any additional tables like or might be needed on the company's requirement and could be left-joined in. The relationships established in the above design are properly normalized and indexing considerations have been made to enhance speed & efficiency.

### SQL Question 5: What are SQL constraints, and can you give some examples?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:

Say you were storing sales analytyics data from Northwestern Mutual's CRM inside a database. Here's some example constraints you could use:

• PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.

• FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.

• NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.

• UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.

• CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.

• DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"

### SQL Question 6: Identify High-Value Customers

Northwestern Mutual, a finance company, would like to identify their high-value customers for better servicing and marketing programs. These are customers that have invested more than \$5000 in the company’s long-term insurance plans. Furthermore, Northwestern Mutual is interested only in those high-value customers who have not lodged any claims in the last two years and their insurance policies are still active.

Design an SQL query to extract the records of these high-value customers.

Given the following tables, for customers, investments and claims:

##### Example Input:
customer_idcustomer_namepolicy_status
2001John DoeActive
2002Jane DoeInactive
2003Mary JohnsonActive
2004James SmithActive
##### Example Input:
customer_idinvestment_amount
20017500
20022000
20036000
20043000
##### Example Input:
customer_idclaim_dateclaim_amount
200106/25/20210
200205/14/20221000
200408/22/2020500

One possible SQL query can look like this:

This query first JOINs the and tables on the fields. Then it LEFT JOINs the table, so that customers without claims still appear in the result. The WHERE clause then filters out customers with investments over \$5000, active status, and either zero claims in the last two years (based on the date given as '01/01/2021') or a null claim_date (indicating no claims ever).

### SQL Question 7: What does do?

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

For a concrete example, say you were doing an HR Analytics project for Northwestern Mutual and needed to analyze both Northwestern Mutual's employees and contractors who were hired after 2022 started. You could use in the following way:

### SQL Question 8: Average Product Insurance Value

As a data analyst at Northwestern Mutual, your task is to find out the average value insured per product across all clients. The 'products' table captures the details of insurance products along with their respective Ids. The 'insurances' table keeps track of the insurance policies sold to clients, including the product_id, client_id, and the sum insured.

##### Example Input:
product_idproduct_name
1Term Life Insurance
2Whole Life Insurance
3Variable Universal Life Insurance
##### Example Input:
insurance_idproduct_idclient_idsum_insured
101111100000
102112150000
103213200000
104214250000
105315300000

This query joins the 'products' and 'insurances' tables on the product_id, then groups by product_name. The average sum insured for each product is calculated with AVG(i.sum_insured).

##### Example Output:
product_nameaverage_insured_amount
Term Life Insurance125000
Whole Life Insurance225000
Variable Universal Life Insurance300000

This output shows the average insured amount per product, allowing us to understand the average value insured for each insurance product provided by Northwestern Mutual.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating product-based metrics or this Amazon Average Review Ratings Question which is similar for getting an average value across multiple records.

### SQL Question 9: Calculating Click-through-rate for Northwestern Mutual Ads

As a financial service provider, Northwestern Mutual often places digital ads to attract new clients. The company wants to learn how successful these ads are by calculating their click-through-rate (CTR). The CTR is the ratio of users who click on a specific link to the number of total users who view the advertisement.

For simplicity, let's assume an table and a table. The table contains information about each of the ads (a unique ID and the date it was displayed), and the table tracks whenever a user clicks on one of the displayed ads.

##### Example Input:
106/08/2022 00:00:00
206/10/2022 00:00:00
306/18/2022 00:00:00
407/26/2022 00:00:00
507/05/2022 00:00:00
##### Example Input:
6191306/18/2022 00:00:101001
7801206/10/2022 00:00:051002
5212407/26/2022 00:00:001003
6314507/05/2022 00:00:091004
4502507/05/2022 00:00:061005

The task is to write a SQL query that calculates the Click-Through-Rate for each ad as a percentage rounded up to the nearest whole number.

The above query joins the and table on . It counts the number of clicks for each ad, which is then divided by the total number of distinct ads to calculate the CTR. The part of the query calculates the CTR as a percentage, and the function rounds it up to the nearest whole number.

To practice a related problem on DataLemur's free interactive SQL code editor, try this Facebook SQL Interview question:

### SQL Question 10: What is the purpose of a primary key in a database?

The primary key of a table is a column or combination of columns that serves to uniquely identify each row in the table. To define a primary key in a SQL database, you can use the constraint.

For instance, consider a table of :

In this example, the column is the primary key of the Northwestern Mutual employees table.

Primary keys are important in databases for several reasons:

• Uniqueness: A primary key is used to uniquely identify each row in a table. This means that no two rows in the table can have the same primary key value. This is important because it helps to ensure the accuracy and integrity of the data in the table.
• Non-nullability: A primary key is typically defined as a non-null column, which means that it cannot contain a null value. This helps to ensure that every row in the table has a unique identifier.
• Relationship-building: Primary keys are often used to establish relationships between tables in a database. For example, you might use a primary key to link the table to the table.

### Northwestern Mutual SQL Interview Tips

The best way to prepare for a Northwestern Mutual SQL interview is to practice, practice, practice. Besides solving the above Northwestern Mutual SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Netflix, Airbnb, and Amazon.

Each SQL question has multiple hints, step-by-step solutions and most importantly, there's an interactive coding environment so you can easily right in the browser your SQL query and have it checked.

To prep for the Northwestern Mutual SQL interview it is also wise to solve SQL problems from other insurance companies like:

Explore the latest news and announcements from Northwestern Mutual and see how they're making a difference!

But if your SQL foundations are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.

This tutorial covers SQL concepts such as aggregate window functions and RANK vs. DENSE RANK – both of these show up often during SQL job interviews at Northwestern Mutual.

### Northwestern Mutual Data Science Interview Tips

#### What Do Northwestern Mutual Data Science Interviews Cover?

In addition to SQL interview questions, the other types of problems to practice for the Northwestern Mutual Data Science Interview include:

#### How To Prepare for Northwestern Mutual Data Science Interviews?

To prepare for Northwestern Mutual Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs
• a crash course covering SQL, AB Testing & ML
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Also focus on the behavioral interview – prep for it using this guide on acing behavioral interviews.