10 Levi Strauss SQL Interview Questions (Updated 2024)
Data Analysts and Data Engineers at Levi Strauss code up SQL queries all the time as part of their job. They use SQL for sales trends across different regions, and managing inventory. That's the reason behind why Levi Strauss usually asks SQL interview questions.
So, to help you practice for the Levi Strauss SQL interview, here’s 10 Levi Strauss SQL interview questions – scroll down to start solving them!
10 Levi Strauss SQL Interview Questions
SQL Question 1: Identify Top Customers for Levi Strauss
Levi Strauss, the iconic denim and casual wear jeans brand, wants to analyze its customer database. The company is interested in identifying their power customers, who make frequent purchases and are consistently engaged with the brand.
Power customers for Levi Strauss may be determined through two factors - first, by the total amount spent, and second, by the purchase frequency. For this exercise, consider people spending more than $10,000 in total and making purchases on more than 50 distinct days as power customers.
For context, let's assume we have the table with the following structure:
Example Input:
Instructions: Write a SQL query that lists the IDs of power customers at Levi Strauss.
Answer:
The following PostgreSQL query would provide the desired output:
Explanation:
In this query, we first calculate the total amount spent and the number of distinct order dates for each customer using the and functions in combination with . This creates a window of data for each customer with their total expenditures and number of distinct order days.
The clause in the outer query then filters out the customers who have spent more than $10,000 and have more than 50 distinct order dates.
To practice a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
SQL Question 2: Top Three Salaries
Given a table of Levi Strauss employee salaries, write a SQL query to find the top 3 highest earning employees within each department.
Levi Strauss Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
You can solve this problem interactively on DataLemur:
Answer:
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 code above is confusing, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
SQL Question 3: What are the various forms of normalization?
The three levels of database normalization (also known as normal forms) are:
First Normal Form (1NF):
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each column in a table contains a single value (no lists or containers of data)
- Each row in the table is unique
Second Normal Form (2NF)
- It's in 1st Normal Form
- All non-key attributes are dependent on the primary key
Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.
Third normal form (3NF) if it meets the following criteria:
- It's in 2nd Normal Form
- There are no transitive dependencies in the table.
A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).
Although there is a 4NF and 5NF, you probably don't need to know it for the Levi Strauss interview.
SQL Question 4: Analyzing product stars over time
As an analyst for Levi Strauss, you are asked to analyze the ratings (in stars) over time for each product. The goal is to understand how each product's reviews evolve each month to identify any significant changes and trends. Write a SQL query that can produce the monthly average stars each product received.
Given a table named having the following structure:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
The output should look like the following:
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This query uses the to get the month from the submit_date column, and then it calculates the for each product_id per month. It groups the results by both the month and the product_id to get separate averages for each product every month. Finally, it orders the results by month and product_id.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: What sets UNION apart from UNION ALL?
The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.
For a concrete example, say you were a Data Analyst at Levi Strauss working on a Marketing Analytics project. If you needed to get the combined result set of both Levi Strauss's Google and Facebook ads you could execute this SQL query:
The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.
SQL Question 6: Average Sale Quantity Of Jeans
For a company like "Levi Strauss", they might want to understand the average quantity of jeans they sell per transaction. In this question, you're asked to find the average quantity of jeans sold per transaction.
Suppose we have the following table:
Example Input:
| transaction_id | customer_id | transaction_date | product_id | quantity |
|---|---|---|---|---|
| 1234 | 567 | 01/01/2022 | 898 | 25 |
| 2345 | 896 | 01/02/2022 | 898 | 30 |
| 5678 | 435 | 01/03/2022 | 898 | 20 |
| 1235 | 765 | 01/04/2022 | 898 | 10 |
| 5679 | 654 | 01/05/2022 | 898 | 15 |
Example Input:
| product_id | product_name |
|---|---|
| 898 | Levi's Jeans |
Here, we assume:
- Each row in represent a single transaction that a customer made on a particular date.
- Each transaction could include multiple quantities of a product.
Example Output:
| product_name | avg_quantity |
|---|---|
| Levi's Jeans | 20 |
The average is calculated by dividing total quantity sold by the total number of transactions.
Answer:
The query finds the corresponded product_name from table based on the product_id in table then calculate the average quantity of Levi's Jeans sold per transaction.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for using sales data to determine performance or this Walmart Histogram of Users and Purchases Question which is similar for analyzing transaction-specific data.
SQL Question 7: What's a database view?
Views are a lot like virtual tables, where you can take a base table and customize it (such as by hiding some data from non-admin users, or removing some random columns/rows based on business requirements).
Here's the PostgreSQL syntax for creating a view based on data in the table:
Show off your knowledge during the interview and read up on the Levi's recent pressroom publications!
SQL Question 8: Calculate the Maximum Sales of Each Product Category in Each Store
As a company specializing in denim jeans, Levi Strauss operates numerous stores worldwide and sells a range of product categories. The following question requires you to determine the store and product category that has the highest sales within a given period.
Given the table below:
Example Input:
| sale_id | store_id | sale_date | product_category | sale_amount |
|---|---|---|---|---|
| 1001 | 501 | 2022/01/01 | Jeans | 2500 |
| 1002 | 502 | 2022/01/02 | Shirts | 1200 |
| 1003 | 501 | 2022/01/02 | Jeans | 2000 |
| 1004 | 503 | 2022/02/01 | Accessories | 1500 |
| 1005 | 502 | 2022/02/02 | Jeans | 1800 |
| 1006 | 503 | 2022/02/03 | Shirts | 1000 |
| 1007 | 502 | 2022/02/04 | Accessories | 1300 |
Write a SQL query to find the highest sales of each product category in each store within January and February 2022.
Answer:
Example Output:
| store_id | product_category | max_sale |
|---|---|---|
| 501 | Jeans | 2500 |
| 502 | Shirts | 1200 |
| 503 | Accessories | 1500 |
| 502 | Jeans | 1800 |
| 503 | Shirts | 1000 |
| 502 | Accessories | 1300 |
This query groups the sales data by store and product category, and then uses the function to find the highest sales within each group. The clause filters data to only consider sales from January and February 2022.
SQL Question 9: FILTER CUSTOMER RECORDS MATCHING PRODUCT PATTERN
As a database manager at Levi Strauss, you are tasked with filtering and retrieving all customer records of a certain product category from the customer database. The product category should match a specific pattern.
For the purpose of this problem, you are to write a SQL query that selects all customers who have purchased jeans - the record of which is stored in the product category as 'LSJ%' where LSJ stands for Levi Strauss Jeans and the '%' means there can be anything after LSJ in the string.
Example Input:
| customer_id | first_name | last_name | product_category |
|---|---|---|---|
| 1 | John | Doe | LSJ001 |
| 2 | Jane | Doe | LSC002 |
| 3 | Jim | Beam | LSJ003 |
| 4 | Jack | Daniels | LSW004 |
| 5 | Jill | Bean | LSJ005 |
Example Input:
| purchase_id | customer_id | product_id | purchase_date |
|---|---|---|---|
| 1 | 1 | LSJ001 | 2021-06-20 |
| 2 | 2 | LSC002 | 2021-07-20 |
| 3 | 3 | LSJ003 | 2021-08-20 |
| 4 | 4 | LSW004 | 2021-09-20 |
| 5 | 5 | LSJ005 | 2021-10-20 |
Example Output:
| customer_id | first_name | last_name | product_category |
|---|---|---|---|
| 1 | John | Doe | LSJ001 |
| 3 | Jim | Beam | LSJ003 |
| 5 | Jill | Bean | LSJ005 |
Answer:
In this query, we first join the customers table (referred to as c) with the purchase table (referred to as p) based on the common column, 'customer_id'. Then we filter the joined table to keep only the records where the 'product_category' starts with 'LSJ'. This way, we retrieve all customer records who have purchased any type of Levi Strauss Jeans.
SQL Question 10: What's a primary key?
The primary key of a table is a column or set of columns that serves as a unique identifier for each row. It ensures that all rows are distinct and does not allow null values.
For example, say you had stored some Facebook ad campaign data that Levi Strauss ran:
The CampaignID column is used to uniquely identify each row in the table, and the constraint ensures that there are no duplicate CampaignID values. This helps to maintain the accuracy of the data by preventing duplicate rows. The primary key is also an important part of the table because it enables you to easily identify and reference specific campaigns in your Facebook Ad data. You can use it to join to other tables in the database, such as a table with data on the results of the campaigns.
Preparing For The Levi Strauss SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Besides solving the earlier Levi Strauss SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each SQL question has multiple hints, step-by-step solutions and most importantly, there is an online SQL coding environment so you can instantly run your SQL query answer and have it checked.
To prep for the Levi Strauss SQL interview it is also a great idea to practice SQL problems from other apparel companies like:
But if your SQL coding skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as math functions and CTE vs. Subquery – both of which pop up often during Levi Strauss SQL interviews.
Levi Strauss Data Science Interview Tips
What Do Levi Strauss Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions tested in the Levi Strauss Data Science Interview include:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product & Business Analytics Questions
- Machine Learning Questions
- Behavioral Interview Questions
How To Prepare for Levi Strauss Data Science Interviews?
The best way to prepare for Levi Strauss 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 Crash Course covering Python, SQL & ML
- Great Reviews (1000+ reviews, 4.5-star rating)