9 Deckers Outdoor SQL Interview Questions (Updated 2024)
Deckers Outdoor employees use SQL daily for analyzing sales trends across different seasons and analyzing shipping and inventory data. Because of this, Deckers Outdoor covers SQL questions during interviews for Data Science and Data Engineering positions.
So, to help you practice, here’s 9 Deckers Outdoor SQL interview questions – able to solve them?
9 Deckers Outdoor SQL Interview Questions
SQL Question 1: Calculate Average Monthly Ratings Per Product
Deckers Outdoor, a footwear designer and distributor, wants to analyze customer sentiment towards their products on a time series. Specifically, they are interested in assessing the average monthly rating for each of their products.
Using the reviews database which includes fields like , , , , and , write a SQL query to calculate the average monthly rating for each product.
Use the following sample data for the problem:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 001 | 743 | 2022-01-15T00:00:00Z | A | 3 |
| 002 | 152 | 2022-01-20T00:00:00Z | A | 4 |
| 003 | 965 | 2022-01-29T00:00:00Z | B | 5 |
| 004 | 678 | 2022-02-15T00:00:00Z | A | 2 |
| 005 | 239 | 2022-02-18T00:00:00Z | B | 3 |
| 006 | 345 | 2022-03-05T00:00:00Z | A | 3 |
| 007 | 657 | 2022-03-15T00:00:00Z | B | 4 |
Answer:
This PostgreSQL window function partitions the reviews data by both month and product id. The function is used to simplify the date to the nearest month. Then, for each of these specific groups (i.e., per month and per product level), the query calculates the average stars. The result is a table where each row represents a unique month-product combination and the corresponding average rating for such combination.
To practice another window function question on DataLemur's free interactive SQL code editor, try this Amazon SQL question asked in a BI Engineer interview:
Read about Decker Brands 2024 financial results!
SQL Question 2: Second Highest Salary
Assume you had a table of Deckers Outdoor employee salary data. Write a SQL query to find the 2nd highest salary amongst all the .
Deckers Outdoor Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Code your solution to this question interactively on DataLemur:
Answer:
You can find a detailed solution with hints here: 2nd Highest Salary.
SQL Question 3: What does the constraint do, and when might you use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 4: Analyze Customer Purchasing Behavior
Given the customer orders and product details data, Deckers Outdoor would like to identify the customers from California who bought hiking shoes more than once in the year 2021. This will allow them to target specific product promotions to this customer segment.
Below are the database tables for consideration:
Example Input:
| customer_id | first_name | last_name | state |
|---|---|---|---|
| 5021 | John | Doe | California |
| 7842 | Jane | Smith | New York |
| 6532 | Mike | Brown | California |
| 3725 | Sara | Johnson | Texas |
| 2178 | Lisa | Taylor | California |
Example Input:
| product_id | product_type |
|---|---|
| 101 | Hiking shoes |
| 102 | Sandals |
| 103 | Boots |
| 104 | Slippers |
| 105 | Trail running shoes |
Example Input:
| order_id | customer_id | product_id | order_date |
|---|---|---|---|
| 201 | 5021 | 101 | 02/01/2021 |
| 202 | 7842 | 102 | 03/22/2021 |
| 203 | 6532 | 101 | 04/14/2021 |
| 204 | 6532 | 101 | 11/26/2021 |
| 205 | 2178 | 101 | 07/30/2021 |
Answer:
This query first extracts the customers who purchased 'Hiking shoes' more than once in 2021, by utilizing a subquery with and . Then it joins this information with the customers table to pull in the customer names and filters for customers residing in 'California'.
SQL Question 5: When considering database normalization, how do 1NF, 2NF, and 3NF differ from one another?
Normal forms are guidelines that are used to help design a relational database in a way that minimizes redundancy and ensures the integrity of the data. The 3 most commonly use normal forms are the 1st, 2nd, and 3rd normal forms. Here's a brief explanation of each:
-
1st Normal Form (1NF) is all about keeping it simple - each column should only have one value and there should be no repeating groups of data.
-
2nd Normal Form (2NF) is about organization - your database should already be in 1NF and all the non-key columns should depend on the primary key. This means that each non-key column should be completely dependent on the entire primary key, not just part of it.
-
3rd Normal Form (3NF) is about independence - if your database is already in 2NF, then all the non-key columns should not depend on each other. They should be self-sufficient and not rely on other non-key columns.
SQL Question 6: Calculate Click-Through-Rate on Ads
As an SQL interviewee at Deckers Outdoor, you might be asked to analyze the click-through rates of their digital marketing campaign.
Assume that Deckers Outdoor uses two tables to store data about digital ads and user actions. The table contains information about each ad including its id and product_id. The table stores data about user actions including the ad they clicked on, the time the click happened, and the page they visited after clicking (perhaps 'product_page', 'home', or 'cart').
Example Input:
| ad_id | product_id |
|---|---|
| 1 | 50001 |
| 2 | 50001 |
| 3 | 69852 |
| 4 | 69852 |
Example Input:
| action_id | user_id | action_time | ad_id | page_visited |
|---|---|---|---|---|
| 101 | 123 | 06/08/2022 00:00:00 | 1 | 'product_page' |
| 102 | 265 | 06/10/2022 00:00:00 | 4 | 'cart' |
| 103 | 362 | 06/18/2022 00:00:00 | 1 | 'home' |
| 104 | 192 | 07/26/2022 00:00:00 | 3 | 'product_page' |
| 105 | 981 | 07/05/2022 00:00:00 | 3 | 'cart' |
Calculate the click-through-rate from the product page to the cart page for each product.
Answer:
Here is your PostgreSQL query to solve the problem:
This query first joins and on . The statement calculates the counts of unique views on product pages and cart adds for each product. It then calculates the click-through-rate as the ratio of cart adds to product page views. The function is used to handle the case where there are zero product page views to avoid a division by zero error.
To solve a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's interactive SQL code editor:
SQL Question 7: How do relational and non-relational databases differ?
A non-relational (NoSQL) database is any database that does not use the typical tabular format of rows and columns like in relational databases.
While knowing the four different types of NoSQL databases is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at Deckers Outdoor should vaguely refresh these concepts:
- Document Databases – this database is designed for storing and querying retrieving document data (where each key is associated with a flexible document)
- Key-Value Stores – these databases uses keys where each key is associated with only one value in a collection (similar to a Python dictionary data structure!)
- Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row within the same table. Remember: "-With great flexibility comes great responsiblity-" – Batman if he was learning about NoSQL databases!
- Graph Stores – represents data in terms of the graph data structure, with nodes and edges between entities
SQL Question 8: Analyzing Sales Data
Given a database with and tables, calculate the total sales amount of each brand for each quarter in 2022.
The table contains information about the products sold by Deckers Outdoor, including the product_id, name, and the brand associated. The table contains data about each sale, including the sale_id, product_id, sale_date, and sale_amount.
Example Input:
| product_id | name | brand |
|---|---|---|
| 1 | Coolfoot Sandals | UGG |
| 2 | Hiker Boots | Teva |
| 3 | City Slick Shoes | Hoka |
Example Input:
| sale_id | product_id | sale_date | sale_amount |
|---|---|---|---|
| 1 | 1 | 01/02/2022 | $200 |
| 2 | 2 | 02/10/2022 | $500 |
| 3 | 1 | 04/15/2022 | $250 |
| 4 | 3 | 07/21/2022 | $300 |
| 5 | 3 | 08/09/2022 | $350 |
Answer:
This query calculates the total sales amount for each brand per quarter in 2022. To do so, it first extracts the quarter and year parts from the using the function. It joins the and tables on to connect a sale to a brand. It groups the data by quarter and brand to enable aggregate calculations of .
Example Output:
| quarter | brand | total_sales |
|---|---|---|
| 1 | UGG | $200 |
| 1 | Teva | $500 |
| 2 | UGG | $250 |
| 3 | Hoka | $650 |
SQL Question 9: Analyzing Customer and Product Data
Given a database of customers and products, construct a SQL query that finds out the average amount spent by each customer by joining the table with the table.
Please consider the following tables:
Customers:
|customer_id|first_name|last_name|email|Created_at| |:----|:----|:----|:----|:----| |101|John|Doe|johndoe@email.com|2020-07-24 17:18:10| |102|Jane|Doe|janedoe@email.com|2021-06-18 11:34:45| |103|Mike|Smith|mikesmith@email.com|2019-05-26 14:45:55| |104|Emma|Johnson|emmajohnson@email.com|2021-01-30 16:18:55| |105|Robert|Brown|robertbrown@email.com|2018-03-15 09:34:21|
Orders:
|order_id|product_id|customer_id|amount|order_date| |:----|:----|:----|:----|:----| |201|1|101|300|2022-06-06 10:45:10| |202|2|101|400|2022-06-07 11:20:30| |203|3|102|150|2022-06-08 14:34:15| |204|4|103|200|2022-06-09 17:18:55| |205|5|104|250|2022-06-10 18:34:21|
The tables and can be joined on the column.
Answer:
PostgreSQL Query:
This query will return a result set with the first and last names of the customers along with the average amount spent by them. The operation is performed on the field which is common in both the tables. The clause is used to group the result set by the customer names and the function is used to calculate the average amount spent by each customer.
Because join questions come up so often during SQL interviews, practice this SQL join question from Spotify:
How To Prepare for the Deckers Outdoor SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Deckers Outdoor SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier Deckers Outdoor SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each problem on DataLemur has hints to guide you, full answers and crucially, there's an online SQL coding environment so you can easily right in the browser your SQL query answer and have it graded.
To prep for the Deckers Outdoor SQL interview you can also be wise to practice SQL questions from other apparel companies like:
In case your SQL foundations are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers SQL topics like filtering strings based on patterns and math functions like CEIL()/FLOOR() – both of which come up frequently during SQL job interviews at Deckers Outdoor.
Deckers Outdoor Data Science Interview Tips
What Do Deckers Outdoor Data Science Interviews Cover?
In addition to SQL interview questions, the other question categories to prepare for the Deckers Outdoor Data Science Interview include:
- Experimental Design & AB Testing Questions
- Python or R Programming Questions
- Open-Ended Data Case Studies
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Deckers Outdoor Data Science Interviews?
To prepare for Deckers Outdoor Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from Microsoft, Amazon & startups
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