10 Brown-Forman SQL Interview Questions (Updated 2024)
At Brown-Forman, SQL is used to analyze sales data, allowing them to optimize their marketing strategies based on customer preferences and trends. They also rely on SQL to manage inventory data, which helps improve supply chain efficiency by ensuring that products are available when they are needed, this is why Brown-Forman asks SQL coding questions in interviews for Data Analyst, Data Science, and BI jobs.
To help prep you for the Brown-Forman SQL interview, here's 10 Brown-Forman SQL interview questions – can you solve them?
10 Brown-Forman SQL Interview Questions
SQL Question 1: Product Reviews Analysis for Brown-Forman
As a data analyst at Brown-Forman, you have been tasked with analyzing product reviews. Specifically, you need to construct a SQL query that will each product's average review ratings, as well as its rank in terms of the number of reviews it had, within each month. The average should be rounded to two decimal places.
We have a table on hand that contains product reviews data.
Given the following tables:
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 |
Create a data set that calculates the average rating per product per month and ranks each product within each month by the number of reviews.
The output of your query should look something like:
Expected Output:
| month | product_id | avg_stars | rank |
|---|---|---|---|
| 6 | 50001 | 3.50 | 2 |
| 6 | 69852 | 4.00 | 1 |
| 7 | 69852 | 2.50 | 1 |
Answer:
This query first extracts the month from the of each product review and groups by it along with the . It calculates the average per product per month and rounds it to two decimal places. It also calculates a rank for each product within each month based on the number of reviews it received that month using the window function with to get the ranking in descending order of the review count. The results are then ordered by the month and the rank.
To practice a similar window function interview problem which uses RANK() on DataLemur's free interactive coding environment, try this Amazon SQL question asked in a BI Engineer interview:
Check out Brown-Forman's news section to learn about their latest product launches and sustainability initiatives in the beverage industry! Following Brown-Forman's updates can provide valuable insights into how they are adapting to consumer preferences and trends.
SQL Question 2: Top Three Salaries
Given a table of Brown-Forman employee salary data, write a SQL query to find the top 3 highest paid employees in each department.
Brown-Forman 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 |
Solve this interview question and run your code right in DataLemur's online SQL environment:
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 solution above is hard to understand, you can find a step-by-step solution here: Top 3 Department Salaries.
SQL Question 3: In SQL, are values same the same as zero or a blank space?
NULLs are NOT the same as zero or blank spaces in SQL. NULLs are used to represent a missing value or the absence of a value, whereas zero and blank space are legitimate values.
It's important to handle NULLs carefully, because they can mess up your analysis very easily. For example, if you compare a NULL value using the = operator, the result will always be NULL (because just like Drake, nothing be dared compared to NULL). That's why many data analysis in SQL start with removing NULLs using the function.
SQL Question 4: Average Sales of Alcohol Brands per month
Brown-Forman is a leading American spirits and wine business company manufacturing several popular brands. Let's assume that the company is interested in understanding their average sales per month on each of their product brands; they want this data to track their performance and strategize their future marketing strategies.
As a database designer, your task is to model tables and come up with a solution to provide them with this crucial data.
Example Input:
| sales_id | product_id | transaction_date | quantity_sold |
|---|---|---|---|
| 1 | 101 | 06/07/2022 | 150 |
| 2 | 202 | 06/10/2022 | 175 |
| 3 | 303 | 06/15/2022 | 200 |
| 4 | 101 | 07/01/2022 | 100 |
| 5 | 202 | 07/04/2022 | 300 |
Example Input:
| product_id | brand |
|---|---|
| 101 | Jack Daniel's |
| 202 | Woodford Reserve |
| 303 | Old Forester |
Answer:
This query breaks down the sales data into months () and calculates the average quantity sold () of each brand in each month. The operation ensures that the proper brand name from the table is associated with each transaction in the table. The statement is used to aggregate the sales data on a monthly basis, for each brand. The statement is used to sort the final result first by month (in ascending order), then by brand (in alphabetical order).
SQL Question 5: How do you locate records in one table that are absent from another?
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here is an example using two tables, Brown-Forman employees and Brown-Forman managers:
This will return all rows from Brown-Forman employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will retrieve all rows from employees that do not appear in managers. The operator works by retreiving the rows that are returned by the first query, but not by the second.
Please note that is not supported by all DBMS systems, such as MySQL and Oracle (however, you can use the operator to achieve a similar outcome).
SQL Question 6: Filter Customer Records and Calculate Average Spend
You are a data analyst at Brown-Forman, a major company in the beverage industry. The sales team would like to do an analysis of the customer's purchasing habits. They wish to extract the records of customers who bought whiskey products multiple times in the month of August and spent on average more than $100 per transaction.
Create the SQL query to accomplish this, taking into account that a transaction might include multiple products.
Example Input:
| transaction_id | customer_id | transaction_date | product_id | product_category | product_price | quantity |
|---|---|---|---|---|---|---|
| 101 | 1 | 08/01/2022 | 200 | Whiskey | 125 | 2 |
| 202 | 1 | 08/15/2022 | 250 | Whiskey | 105 | 1 |
| 303 | 2 | 08/20/2022 | 200 | Whiskey | 125 | 1 |
| 404 | 2 | 08/25/2022 | 250 | Whiskey | 105 | 2 |
| 505 | 3 | 08/15/2022 | 200 | Whiskey | 125 | 1 |
| 606 | 3 | 08/30/2022 | 250 | Whiskey | 105 | 1 |
Example Output:
| customer_id | average_spend |
|---|---|
| 1 | 170.00 |
| 2 | 167.50 |
Answer:
This query first filters the table for records pertaining to the category within the month of August. It then groups by , but only keeps groups that made more than one purchase and had an average total spend per transaction above $100.
SQL Question 7: What does the SQL command do?
If you have two tables and want to retrieve only the rows that are present in both tables, just use the operator!
For example, let's use to find all of Brown-Forman's Facebook video ads that are also being run on YouTube:
SQL Question 8: Calculation of Click-Through-Rates for Brown-Forman
Imagine you are a data analyst at Brown-Forman, a major spirits and wine business. The marketing team has been running online advertising campaigns for various products and wants to evaluate their effectiveness. Specifically, they're interested in understanding the click-through-rates (CTR) from the ad to the product's page on Brown-Forman's website.
Here are the datasets you have at your disposal:
Example Input:
| ad_id | product_id | campaign_id |
|---|---|---|
| 001 | BF01 | R01 |
| 002 | BF02 | R01 |
| 003 | BF03 | R02 |
| 004 | BF01 | R02 |
| 005 | BF02 | R03 |
Example Input:
| click_id | ad_id | click_timestamp |
|---|---|---|
| 1250 | 001 | 01/10/2022 09:00:00 |
| 3421 | 003 | 01/10/2022 11:00:00 |
| 4533 | 001 | 01/11/2022 12:00:00 |
| 9862 | 002 | 01/13/2022 17:00:00 |
| 4534 | 004 | 01/14/2022 09:00:00 |
| 6352 | 005 | 01/15/2022 12:00:00 |
Example Input:
| impression_id | ad_id | impression_timestamp |
|---|---|---|
| 5501 | 001 | 01/10/2022 08:00:00 |
| 2502 | 003 | 01/10/2022 10:00:00 |
| 8473 | 001 | 01/11/2022 11:00:00 |
| 6872 | 002 | 01/13/2022 16:00:00 |
| 8634 | 004 | 01/14/2022 08:00:00 |
| 9862 | 005 | 01/15/2022 11:00:00 |
Write a query that calculates the CTR for each ad, where CTR is defined as the total number of clicks on the ad divided by the total number of impressions.
Answer:
In this query, we first join all three tables on . We then count the total number of unique clicks and impressions for each ad. The CTR is then calculated by dividing the number of unique clicks by the number of unique impressions, and multiplying by 100 to get the percentage. To avoid division by zero errors, we use to replace any zero denominators with NULL. The results are grouped by and .
To solve a similar problem on DataLemur's free online SQL coding environment, try this SQL interview question asked by Facebook:
SQL Question 9: Search for Customers with a Specific Pattern of Name
Brown-Forman is a large spirits and wine company. For marketing and customer service purpose, they want to find all customers whose names contain "Lee". Generate the list of these customers from their customer database.
Assume you have the following table:
Example Input:
| customer_id | first_name | last_name | phone_number | registration_date | |
|---|---|---|---|---|---|
| 101 | Jessica | Lee | jessica_lee@example.com | (555)555-5555 | 01/10/2022 09:00:00 |
| 102 | Brian | Green | brian_green@example.com | (555)555-5556 | 01/03/2022 10:00:00 |
| 103 | Clark | Lee | clark_lee@example.com | (555)555-5557 | 01/05/2022 11:00:00 |
| 104 | Julie | Leeson | julie_leeson@example.com | (555)555-5558 | 01/07/2022 09:00:00 |
| 105 | Richard | Klee | richard_klee@example.com | (555)555-5559 | 01/09/2022 10:00:00 |
Answer:
This PostgreSQL query scan the table and filters for the records where the or contains the string 'Lee'. The LIKE keyword in the WHERE clause is used for pattern matching. The percentage (%) character is a wildcard character that matches any sequence of characters. This query will match any records where the string 'Lee' appears anywhere in the or fields.
SQL Question 10: What does the constraint do?
A is like a secret code that unlocks the door to another table. It's a field in one table that points to the (the master key) in another table. This helps keep the data in your database organized and tidy, because it won't let you add new rows to the table unless they have the correct secret code (a corresponding entry in the table).
It's also like a special bond between the two tables - if you try to delete the data, the will be like "Whoa, hold on! I still need that information!" and prevent the deletion from happening.
Preparing For The Brown-Forman 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. In addition to solving the above Brown-Forman SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like tech companies and food and facilities companies like Brown-Forman.
Each DataLemur SQL question has multiple hints, step-by-step solutions and best of all, there is an interactive coding environment so you can easily right in the browser your query and have it checked.
To prep for the Brown-Forman SQL interview you can also be a great idea to solve SQL problems from other food and facilities companies like:
- Lamb Weston Holdings SQL questions
- Flowers Foods SQL questions
- TreeHouse Foods SQL questions
- Darling Ingredients SQL questions
However, if your SQL coding skills are weak, forget about diving straight into solving questions – go learn SQL with this free SQL tutorial.
This tutorial covers things like HAVING and handling dates – both of which come up often during Brown-Forman SQL interviews.
Brown-Forman Data Science Interview Tips
What Do Brown-Forman Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems tested in the Brown-Forman Data Science Interview are:
- Probability & Stats Questions
- Python Pandas or R Coding Questions
- Product-Sense Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for Brown-Forman Data Science Interviews?
To prepare for the Brown-Forman Data Science interview have a strong understanding of the company's cultural values – this will be key to acing the behavioral interview. For the technical Data Science interviews, prepare by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Crash Course covering SQL, Product-Sense & ML
- Amazing Reviews (1000+ reviews, 4.5-star rating)