# 11 Polaris SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

At Polaris, SQL is used across the company for analyzing dealership performance data and identifying trends in vehicle parts inventory management. That's the reason behind why Polaris frequently asks SQL problems during interviews for Data Analyst, Data Science, and BI jobs.

As such, to help you practice for the Polaris SQL interview, we've curated 11 Polaris SQL interview questions – scroll down to start solving them!

## 11 Polaris SQL Interview Questions

### SQL Question 1: Calculate the monthly average rating for each product

Polaris is an e-commerce site which sells a variety of items. They collect a large number of product reviews from their users. One of the tables in their database, , stores this data.

Assuming that the table has the following fields:

• : the unique ID of the review
• : the ID of the user who submitted the review
• : the date and time when the review was submitted
• : the ID of the product
• : the rating given by the user, from 1 to 5.

Write a PostgreSQL query that calculates the monthly average rating for each product. The result should include the month of the review (as an integer, from 1 for January to 12 for December), the , and the average . Order the result by month and then by .

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
61711232022-06-08 00:00:00500014
78022652022-06-10 00:00:00698524
52933622022-06-18 00:00:00500013
63521922022-07-26 00:00:00698523
45179812022-07-05 00:00:00698522
##### Example Output:
mthproduct_idavg_stars
6500013.50
6698524.00
7698522.50

The SQL query is:

In the query, is used to get the month from the . calculates the average rating for each product per month. The statement is then used to organize the result according to month and . The result is ordered by month (from 1 for January to 12 for December), then by product.

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

### SQL Question 2: 2nd Highest Salary

Imagine you had a table of Polaris employee salary data. Write a SQL query to find the 2nd highest salary among all employees.

#### Polaris Example Input:

employee_idsalary
12500
2800
31000
41200

#### 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:

You can find a detailed solution here: 2nd Highest Salary.

### SQL Question 3: What would you do to optimize a SQL query that was running slow?

Here's some strategies that can generally speed up a slow SQL query:

• Only query the columns you actually need
• Index the columns used in the query
• Use the right kind of JOIN (be explicit about inner vs. outer joins)
• Use the right data types (for example INT vs BIGINT can save you disk space and memory)

While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at Polaris, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the command and understand a bit more about a query-execution plan.

### SQL Question 4: Sales Analysis for Polaris

As part of the data team at Polaris, which is a manufacturer and provider of power sport vehicles and boats, you have been tasked with analyzing the sales registered for different power sport vehicles and boats. Here, it is significant to track the sales by product type, product id, and sales region over specific months to have a solid understanding of sales trends.

The key details of sales are stored in table and product details are stored in table. Your task is to write a SQL query to fetch the total sales generated for each and by for the month of 'August 2022'.

Given below are the sample tables:

##### Example Input:
sales_idproduct_idsales_datesales_regionsales_amount
10030008/05/2022 00:00:00North5000
10120008/06/2022 00:00:00South4000
10230008/15/2022 00:00:00East3000
10310008/12/2022 00:00:00West8000
10410008/08/2022 00:00:00North7000
##### Example Input:
product_idproduct_type
100Boat
200ATV
300Snowmobile

This SQL query first uses to connect both tables on the common column . A clause is used to filter the sales data for the August 2022. The and functions are used to extract the month and year from the respectively. A clause is applied on and to separate the sales of each product according to their types for each region. The function calculates the total sales for each product type and region for the given time period.

### SQL Question 5: Can you explain the distinction between cross join and natural join?

A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.

For example, say you worked on the Marketing Analytics team at Polaris, 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:

A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.

For an example of each one, say you had sales data exported from Polaris's Salesforce CRM stored in a datawarehouse which had two tables: and .

An (which is a type of natural join) combines the two tables on the common

This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.

One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.

### SQL Question 6: Filter Customers based on Purchase History and Location

Polaris needs to filter its Customer database based on certain conditions for a marketing campaign. Specifically, they need a list of customers who have:

• made at least 5 purchases in the past year,
• bought the product 'Starlight' at least once,
• and live either in 'Hermes City' or 'Apollo City'.

Using the and tables below, write a query that will give them this information.

##### Example Input:
customer_idcustomer_namelocation
1John DoeOlympus City
2Jane DoeHermes City
3Jim DoeApollo City
4June DoeAthena City
5Jack DoeHermes City
##### Example Input:
purchase_idcustomer_idproduct_namepurchase_date
11Moonlight2022-01-01
22Starlight2022-03-01
32Moonlight2021-12-01
42Starlight2022-05-01
52Sunlight2022-01-01
62Moonlight2022-04-01
72Sunlight2022-02-01
83Starlight2022-06-01
93Sunlight2022-03-01
103Sunlight2022-01-01
113Sunlight2022-02-01
123Sunlight2020-02-01
133Sunlight2022-04-01

The query begins by joining the Customers table with a subquery of the Purchases table, where the subquery aggregates total purchases and Starlight purchases for each customer in the past year. The WHERE clause then filters customers based on the determined purchase conditions and their locations. This approach ensures that only relevant purchases are considered when counting the number of transactions a customer has made.

### SQL Question 7: What are the main differences between foreign and primary keys in a database?

To explain the difference between a primary key and foreign key, let's start with an example Polaris sales database:

:

+------------+------------+------------+------------+ | order_id | product_id | customer_id| quantity | +------------+------------+------------+------------+ | 1 | 303 | 1 | 2 | | 2 | 404 | 1 | 1 | | 3 | 505 | 2 | 3 | | 4 | 303 | 3 | 1 | +------------+------------+------------+------------+

In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.

and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.

### SQL Question 8: Average Sales Per Product

As a data analyst at Polaris, a company that sells all-terrain vehicles (ATVs), snowmobiles and motorcycles, you are tasked with determining the average monthly sales quantity for each product. You also need to provide the month which had the highest average sale.

Given the tables and , please write a SQL query that will solve the above problem.

##### Example Input:
sale_idproduct_idsale_datequantity
110001/06/20225
210101/06/20227
310002/06/20224
410203/06/20226
510003/06/20228
##### Example Input:
product_idproduct_name
100ATV
101Snowmobile
102Motorcycle
##### Example Output:
monthproductavg_quantity
6ATV6
6Snowmobile7
6Motorcycle6

This query first extracts the month from the sale date. After joining the and tables based on the product_id, it groups the results by month and product. The aggregate function is used to calculate the average quantity sold for each product per month. The results are ordered by avg_quantity in descending order and then by month in case multiple products have the same highest average sale in different months.

### SQL Question 9: Finding Customers from a Specific Region

You're working as a data analyst at Polaris. The marketing team wants to target customers from specific regions for a campaign. They want to target the customers whose addresses contain the string "NY" (New York). Can you use the customer database to help them find out the customers from New York?

##### Example Input:
2345JohnDoejohndoe@gmail.com123 Main St, NY
7643JaneSmithjanesmith@gmail.com456 Elm St, CA
8712BobJohnsonbobjohnson@gmail.com789 Pine St, NY
9034AliceWilliamsalicewilliams@gmail.com321 Oak St, TX
1235CharlieBrowncharliebrown@gmail.com654 Maple St, NY
##### Example Output:
2345JohnDoejohndoe@gmail.com123 Main St, NY
8712BobJohnsonbobjohnson@gmail.com789 Pine St, NY
1235CharlieBrowncharliebrown@gmail.com654 Maple St, NY

This query filters the customer table and returns all records where the address field contains the substring 'NY'. The '%' is a wildcard in SQL that matches any sequence of characters. As we are looking for any address containing 'NY', we use '%NY%' where 'NY' can be at anywhere in the address field.

### SQL Question 10: Can you explain what a cross-join is and the purpose of using them?

A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.

Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Polaris product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Polaris products.

Here's a cross-join query you could use to find all the combos:

Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Polaris had 500 different product SKUs, you'd get 5 million rows as a result!!

Check out the Polaris career page and see what role might be the best fit for you.

### SQL Question 11: Find All Active Customers and their Last Purchase

You are given two tables: containing all customer information and containing purchases made by the customers. Write a SQL query to join these 2 tables and return the list of all active customers and the details of their last purchase.

##### Example Input:
customer_idcustomer_firstnamecustomer_lastnamecustomer_emailactive_status
101JohnDoejohn.doe@example.comactive
102JaneSmithjane.smith@example.comactive
103RobertBrownrobert.brown@example.cominactive
##### Example Input:
purchase_idcustomer_idproduct_idpurchase_date
201102500012022-06-08
202101698522022-06-10
203101500012022-08-01
204103698522022-06-10
205102500012022-08-01

This query groups customers and their purchases together, and then it filters for customers who are listed as 'active.' Secondly, it only takes the most recent purchase for each customer into consideration. The result is sorted by .

Because join questions come up frequently during SQL interviews, try this interactive Snapchat JOIN SQL interview question:

### Polaris SQL Interview Tips

The key to acing a Polaris SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier Polaris SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Facebook, Google, and VC-backed startups.

Each DataLemur SQL question has hints to guide you, full answers and most importantly, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it checked.

To prep for the Polaris SQL interview it is also helpful to practice SQL problems from other automotive companies like:

But if your SQL skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.

This tutorial covers things like RANK vs. DENSE RANK and filtering with LIKE – both of which come up often in SQL interviews at Polaris.

### Polaris Data Science Interview Tips

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

In addition to SQL interview questions, the other types of questions to prepare for the Polaris Data Science Interview are:

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

The best way to prepare for Polaris Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from FAANG (FB, Apple, Amazon, Netflix, Google)
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