# 11 Tapestry SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

Tapestry employees use SQL often for managing customer data for personalized marketing strategies, and tracking supply chain performances. Unsurprisingly this is why Tapestry covers SQL query questions in interviews for Data Analyst, Data Science, and BI jobs.

So, to help you prep, here's 11 Tapestry Inc. SQL interview questions – can you solve them?

## 11 Tapestry Inc. SQL Interview Questions

### SQL Question 1: Identify the VIP customers for Tapestry

Tapestry is a leading luxury lifestyle company. They own multiple brands, such as Coach, Kate Spade and Stuart Weitzman. For this company, VIP customers may be those who place large orders often. Let's define a VIP customer as one who has placed more than 10 orders and those orders' total value is more than \$5000 in the last 30 days. Write a SQL query to find out these customers.

##### Example Input:
order_idcustomer_idorder_datetotal_price
15482392022-08-07350
10654152022-08-11150
35792392022-08-151050
53124152022-08-16200
96156792022-08-17400
##### Example Input:
customer_idfirst_namelast_name
239JohnDoe
415JaneSmith
679JamesBrown

This SQL query first isolates the customer IDs that meet the conditions of VIP within the subquery (the customers who have more than 10 orders and the total value of the orders is more than \$5000 in the last 30 days). Then it matches these customer IDs with the customer IDs in the customers table to get the first name and the last name of the VIP customers. The CURRENT_DATE in the condition is a PostgreSQL function used to get the current date, and the INTERVAL '30 days' is to limit the orders within the last 30 days.

To practice a super-customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:

### SQL Question 2: Top Department Salaries

Suppose there was a table of Tapestry employee salary data. Write a SQL query to find the top 3 highest earning employees in each department.

#### Tapestry 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

Check your SQL query for this problem directly within the browser 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 hard to understand, you can find a detailed solution here: Top 3 Department Salaries.

### SQL Question 3: Could you describe the function of UNION in SQL?

{#Question-3}

In SQL, the operator combines the result of two or more SELECT statements into a single result set. Note that each statement within the UNION must have the same number of columns and the columns must have similar data types. The operator removes duplicates from the final result set. If you want to include duplicates in the final result set, you can use the operator instead.

Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Tapestry's Facebook ads and their Google ads:

This statement would return a combined result set of both Facebook and Google ads that have a spend greater than \$500.

### SQL Question 4: Calculate monthly average rating for each product

You are given a table named and it contains customers' reviews of various products sold by Tapestry. Each row contains a unique review identifier (), the id of the user who submitted the review (), the datetime the review was submitted (), and the id of the product being reviewed (), and the star rating () the user gave the product, which is an integer from 1 (worst) to 5 (best).

Write a SQL query that calculates the monthly average star rating, rounded to two decimal places, for each product.

##### 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:
mthproductavg_stars
6500013.50
6698524.00
7698522.50

In this query, we are first extracting the month from the using the function. Then, we group by the month and to calculate the average for each product per month. We use the function to round the average to 2 decimal places.

To solve a related window function SQL problem on DataLemur's free interactive coding environment, solve this Google SQL Interview Question:

BTW Tapestry Inc. has TONS of brands under its umbrella, make sure you familiarize yourself with them before the interview!!

### SQL Question 5: Can you explain the purpose of the constraint?

A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.

Say for example you had sales analytics data from Tapestry's CRM (customer-relationship management) tool.

In this example, the table has a foreign key field called that references the "account_id" field in the table (the primary key). This helps to link the data about each opportunity to the corresponding account information in the accounts table.

This makes sure the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and ensures that data is not deleted from the table if there are still references to it in the table.

### SQL Question 6: Product Sales Analysis

Tapestry, a multinational luxury fashion holding company, wants to conduct a sales analysis of their various products across different brands and regions. They need information on which product category performs the best in which region. They want to identify the total amount of sales that each product category garnered, in each region, for the current year.

This analysis is supposed to help the company strategize their marketing efforts and identify opportunities for improvement or expansion.

DataBase design consists of two tables: and .

##### Example Input:
product_idbrandcategory
35601CoachBags
20698Stuart WeitzmanShoes
73548CoachAccessories
58132Stuart WeitzmanBags
##### Example Input:
sale_idproduct_idsale_dateregionquantityprice
50001356012022-06-12North America1000200
40002843522022-05-08Asia60075
30003206982022-04-22Europe800150
20004735482022-03-15North America70090
10005581322022-02-01Asia1200180

Create an SQL query to find the total sales amount (calculated as the product of quantity sold and the sale price) for each product category and each region for the year 2022.

This query joins the and tables on the column then groups the data by and . It sums the product of and for each group to compute the total sales. The result is ordered in descending order by to show categories and regions with the highest sales on top. The clause limits the data to the year 2022.

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

"In SQL, a join generally retrieves rows from multiple tables and combines them into a single result set. For an example of the difference between a left vs. right join, suppose you had a table of Tapestry orders and Tapestry customers.

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

A combines all rows from the right table (in this case, the Customers table) and any matching rows from the left table (the Orders table). If there is no match in the left table, NULL values will be displayed for the left table's columns.

### SQL Question 8: Average Sales of Each Product

Tapestry is a multinational luxury fashion holding company. Suppose we have the sales data for the past several months for its products. Your task is to write a SQL query to find the average sales of each product per month.

##### Example Input:
sales_idproduct_idsales_datequantity_sold
990778906/09/2022 00:00:0015
388245206/15/2022 00:00:0020
147478906/20/2022 00:00:0025
647312307/05/2022 00:00:0030
244512307/14/2022 00:00:0035
##### Example Output:
monthproductavg_sales
June78920
June45220
July12332.50

This query uses the TO_CHAR function to break down the sales_date field into months. It then takes the average of the quantity_sold field for each product in each month. The GROUP BY statement is used to group the results by month and product. You might need to adjust this query depending on the actual structure and data types in your database.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for its focus on item sales or this Amazon Average Review Ratings Question which is similar for its use of average calculations per product.

### SQL Question 9: Calculate Click-Through-Rate (CTR) For Marketing Ad Campaigns

Tapestry, a luxury fashion and lifestyle company, is running various digital marketing campaigns across different platforms. Each ad features a unique product available on their e-commerce website. They record when the ad was viewed by a user and when it was clicked to visit the product page.

Calculate the click-through-rate (CTR) per product for a particular marketing campaign month.

CTR is calculated as the number of unique clicks divided by the number of unique views, expressed as percentage. Use only the log data recorded within the given month.

##### Example Input:
12578198206/10/2022 00:00:00872275323
13537552006/15/2022 00:00:00710128782
15293687206/25/2022 00:00:00872275323
10985895307/02/2022 00:00:00710128782
19872332207/22/2022 00:00:00482257851
##### Example Input:
98741198206/10/2022 00:00:00872275323
87621484406/18/2022 00:00:00872275323
76531895307/03/2022 00:00:00710128782
65842332207/23/2022 00:00:00482257851
55568462107/25/2022 00:00:00482257851

This query extracts the month from the view_date in the ad_views table and groups the unique views and clicks by product_id. The counts of the unique clicks and views are divided to calculate the CTR. LEFT JOIN is used to make sure all views are accounted for even if they didn't lead to a click.

To solve a related problem on DataLemur's free interactive SQL code editor, solve this Meta SQL interview question:

### SQL Question 10: How do cross joins and natural joins differ?

Cross join and natural join are like two sides of a coin in the world of SQL.

Cross joins is like the wild and reckless cousin who creates a giant new table by combining every row from table A with every row from table B, no questions asked, no common key needed.

Natural joins are like the more refined and selective cousin who only combines rows from multiple tables if they have something in common (i.e., common columns/keys).

While cross join doesn't discriminate and will create a massive table if given the chance, natural join is more selective and only returns a table with the number of rows equal to the number of matching rows in the input tables. So, choose your JOIN wisely!

### SQL Question 11: Average Sales of Tapestry Products by Month

Question Description: At Tapestry, a company dealing with a wide range of products, they would like to analyze the monthly performance of each product. Your task is to calculate and display the average sales of each product on a monthly basis.

##### Example Output:

The query works by first using to get the month portion of the sales date. It then averages the for each in each using . The clause groups the results by and , and the clause sorts the results by and .

### How To Prepare for the Tapestry SQL Interview

The best way to prepare for a Tapestry SQL interview is to practice, practice, practice. Beyond just solving the above Tapestry SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Facebook, Google, and VC-backed startups.

Each interview question has multiple hints, step-by-step solutions and best of all, there is an interactive SQL code editor so you can right in the browser run your SQL query and have it checked.

To prep for the Tapestry SQL interview it is also wise to solve interview questions from other apparel companies like:

However, if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers topics including RANK() window functions and GROUP BY – both of these show up often during SQL job interviews at Tapestry.

### Tapestry Inc. Data Science Interview Tips

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

In addition to SQL interview questions, the other types of questions tested in the Tapestry Data Science Interview include:

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

I'm a tad biased, but I think the optimal way to prep for Tapestry Data Science interviews is to read the book I wrote: Ace the Data Science Interview.

The book solves 201 data interview questions sourced from Facebook, Google & startups. It also has a refresher covering SQL, Product-Sense & ML. And finally it's helped thousands of people land their dream job in data, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.