# 10 Monotaro SQL Interview Questions (Updated 2024)

Updated on

March 2, 2024

At Monotaro, SQL is used all the damn time for analyzing customer purchasing patterns and optimizing inventory based on sales predictions. That's why Monotaro often tests SQL coding questions in interviews for Data Analyst, Data Science, and BI jobs.

Thus, to help you prepare for the Monotaro SQL interview, we've curated 10 Monotaro SQL interview questions – can you solve them?

## 10 Monotaro SQL Interview Questions

### SQL Question 1: Calculate Ranked Sales Performance by Department

Monotaro is a company that sells various types of goods across multiple departments. Each sale is recorded in a table and each product belongs to a department, which is identified in a table.

Your task is to write a SQL query that provides the total sales for each department, and also ranks each department by their total sales, with Department 1 having the highest sales.

#### Sample Input:

##### Example Input:
sale_idproduct_idsale_datesale_amount
1100101/01/2022200
2200201/02/2022150
3100201/03/2022500
4300101/04/2022250
5200101/05/2022350
##### Example Input:
product_iddepartment_id
10011
10021
20012
20022
30013

#### Sample Output:

department_idtotal_amountsales_rank
17001
25002
32503

This query first joins the table and the table on the field. Then it groups by to calculate the total sales for each department. The window function is used to rank the departments based on their total sales, in descending order.

Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur

### SQL Question 2: Monitoring and Evaluating Product Sales Performance

Monotaro is a company that sells a range of products. They would like to monitor and evaluate sales performance for each of their products. Specifically, they want to identify the top 5 products for each month according to the total sales quantity.

Assume you have two tables:

which records the sales for each product, with columns:

• (integer, primary key)
• (integer)
• (integer)
• (timestamp)

which stores the product information, with columns:

• (integer, primary key)
• (string)
##### Example Input:
sale_idproduct_idsale_datequantity
100150006/08/2021150
100230006/10/2021120
100350006/18/2021130
100420007/26/2021140
100540007/05/2021180
##### Example Input:
product_idproduct_name
200Product A
300Product B
400Product C
500Product D

You can use the following PostgreSQL query to solve this problem:

This query first groups the sales data by and and calculates the total quantity for each group (CTE ). It then joins the CTE with the table based on to get the corresponding product name. The result is ordered by in ascending order and in descending order, and only the top 5 rows are selected. This gives the top 5 products for each month according to the total sales quantity.

### SQL Question 3: What is a cross-join?

A cross-join, also referred to 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 with a row for each possible combination of rows from the two input tables.

For example, say you worked on the Marketing Analytics team at Monotaro, and were tasked to understand what advertising copy (text) goes best with what advertising creative (the photo that gets used along with the text 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 Monotaro. 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! As a result, it's important to use cross-joins judiciously, and make sure that you're not generating more data than you need to.

### SQL Question 4: Fetch Customers with Certain Attributes.

Monotaro is interested in understanding more about its customer base. Write a SQL query to filter down the customer records based on multiple boolean conditions:

• Customers who are members of the 'Premium' category
• Customers who have made purchases of more than \$500
• Customers who made their last purchase in June 2022.

Use the table that has the following structure:

##### Example Input:
customer_idfirst_namelast_namemembershiplast_purchase_datepurchase_amount
2JaneDoeRegular2022-06-18550.00
5PaulThomasRegular2022-05-18600.00

The PostgreSQL query to solve this problem is:

The SQL query will filter down the list of customers in the 'Premium' membership who have made purchases exceeding \$500, and that the purchase occurred in June 2022. The function is used to consider only the month and year from the column while comparing with the target date.

### SQL Question 5: What's the difference between a clustered and non-clustered index?

Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.

### SQL Question 6: Calculate Average Order Value for Each Customer

As an analyst at Monotaro, a leading B2B eCommerce marketplace for industrial products, you are tasked to find out the average order value for each client. This will help the company understand spending patterns and also determine high value clients.

Below is the given table with relevant fields like , , and .

##### Example Input:
order_idclient_idorder_dateorder_amount
101300106/08/2022 00:00:0015000
102600206/10/2022 00:00:0080000
103300106/18/2022 00:00:0090000
104400307/26/2022 00:00:0060000
105600207/05/2022 00:00:0040000

Based on the dataset provided, this SQL query groups the data by and calculates the average for each client. Round function is used to round the result to two decimal digits.

##### Example Output:
client_idavg_order_value
300152500.00
400360000.00
600260000.00

According to the output, Client 3001 has an average order value of 52,500 and both Client 4003 and 6002 have an average order value of 60,000.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing top products per category or this Stripe Repeated Payments Question which is similar for working with transactional records.

### SQL Question 7: What's the difference between a left and right join?

Both left and right joins in SQL allow you to combine data from different tables based on a shared key or set of keys. For a concrete example of the difference between these two join types, say you had sales data exported from Monotaro's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .

: retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.

• : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.

### SQL Question 8: Analysis of Customer Purchases and Account Registration

Monotaro has two key tables, which records the details of all their registered customers and which records all purchases made by their registered customers. Your task is to write a query that shows the number of purchases made by customers based on the year they registered.

##### Example Input:
customer_idregistration_date
1012017-04-16
1022019-08-22
1032021-01-08
1042018-02-14
1052020-06-25
##### Example Input:
purchase_idcustomer_idpurchase_dateproduct_id
2011012022-03-125001
2021012022-05-166971
2031022022-01-185001
2041032022-02-146971
2051042022-01-065001
2061052022-03-016971

This query first joins the table with the table on the field. It then filters to only include purchases made in the year 2022. The function is used to extract the year from the and . The purchases are then grouped by the year of registration and counted. The final result is ordered by the .

Because join questions come up so often during SQL interviews, practice this SQL join question from Spotify:

### SQL Question 9: Calculate Discount Percentage and Total Sale Amount for Each Product

Monotaro company is providing a discount on each of its products for a promotional event. They keep track of original prices and discounted prices in their products table. Also, they hold a record for each sale in the sales table.

Your task is to write a SQL query to find out the discount percentage for each product, round it to the nearest whole number, and calculate the total sale amount per each product.

##### Example Input:
product_idproduct_nameoriginal_pricediscounted_price
101Screwdriver1000750
102Hammer15001300
103Wrench20001500
104Drill30001950
##### Example Input:
sale_idproduct_idquantitysale_date
1101102022-05-01
210272022-05-01
310352022-05-02
410432022-05-03
510152022-05-03
##### Example Output:
product_idproduct_namediscount_percentagetotal_sale_amount
101Screwdriver2511250
102Hammer139100
103Wrench257500
104Drill355850

This SQL query first joins products and sales tables on the product_id. Then it calculates the discount percentage by subtracting the discounted_price from the original_price, then divides it by the original price, and then multiplies it by 100. The result is then rounded to the nearest whole number using the ROUND() function. SUM() function is used to calculate the total sale amount per each product by multiplying quantity and discounted_price. Finally, it groups the result by product_id, product_name, discount_percentage, and orders it by total_sale_amount in descending order.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating gross amounts for specific items or this Wayfair Y-on-Y Growth Rate Question which is similar for calculating sales growth rates.

### SQL Question 10: List a few of the ways you find duplicate records in a table in SQL.

"One creative way is to use the window function ROW_NUMBER() and partition by whichver columns you are loooking for duplicates in. If any row has a row_number() more than 1, then it's a duplicate!

You could also use COUNT(DISTINCT col1) vs. COUNT(*) and see if they return different numbers, indicating the prescence of duplicates!

### How To Prepare for the Monotaro SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Monotaro SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Monotaro SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Netflix, Airbnb, and Amazon.

Each DataLemur SQL question has multiple hints, full answers and most importantly, there is an interactive SQL code editor so you can right in the browser run your query and have it executed.

To prep for the Monotaro SQL interview it is also helpful to solve interview questions from other tech companies like:

In case your SQL foundations are weak, forget about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.

This tutorial covers SQL concepts such as math functions in SQL and RANK() window functions – both of these show up often during SQL job interviews at Monotaro.

### Monotaro Data Science Interview Tips

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

Beyond writing SQL queries, the other question categories to practice for the Monotaro Data Science Interview are:

• Probability & Stats Questions
• Python or R Programming Questions
• Product-Sense Questions
• ML Interview Questions
• Resume-Based Behavioral Questions

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

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

• 201 Interview Questions from companies like Microsoft, Google & Amazon
• A Refresher covering Python, SQL & ML
• Amazing Reviews (900+ 5-star reviews on Amazon)