10 Flowers Foods SQL Interview Questions (Updated 2024)
Flowers Foods employees write SQL queries to analyze customer purchase patterns, allowing them to create targeted marketing campaigns that effectively reach their audience. They also use SQL to optimize production schedules based on sales data, ensuring they can meet customer needs effectively, that is the reason why Flowers Foods asks SQL coding questions during interviews for Data Science, Data Engineering, and Data Analytics jobs.
Thus, to help you prepare, we've collected 10 Flowers Foods SQL interview questions – able to solve them?
10 Flowers Foods SQL Interview Questions
SQL Question 1: Monthly product analysis using PostgreSQL window function
You are provided with an table that contains information about all orders made in Flowers Foods. The columns include , , , and .
For each product per month, calculate the total sales (quantity sold), and compare it with the previous month.
In the output, show the (in YYYY-MM format), , , and if quantity increased, decreased or remained same as compared to the previous month.
Sample Data:
Example Input:
| order_id | product_id | order_date | quantity |
|---|---|---|---|
| 1 | A | 2022-06-01 | 15 |
| 2 | B | 2022-06-05 | 5 |
| 3 | A | 2022-07-10 | 20 |
| 4 | B | 2022-07-12 | 3 |
| 5 | A | 2022-07-25 | 7 |
| 6 | B | 2022-08-14 | 10 |
| 7 | A | 2022-08-27 | 22 |
Answer:
In this query, we first create a subquery to calculate the total quantity sold for each product for every month. Then we use the PostgreSQL window function in the subquery to get the quantity from the previous month for each product. Finally, we compare the current month's total quantity with the previous month's total quantity and label it as "Increased", "Decreased", or "Same". If there is no data for the previous month, we return 'No previous month data'. We partition the data by and order by to ensure that we compare the quantity for the same product from consecutive months.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
Explore Flowers Foods' newsroom to stay updated on their latest product innovations and community initiatives that are making a difference in the baking industry! Understanding Flowers Foods' efforts can provide insights into how they are connecting with consumers and enhancing their brand presence.
SQL Question 2: Employees Earning More Than Managers
Assume there was a table of Flowers Foods employee salaries. Write a SQL query to find the employees who earn more than their own manager.
Flowers Foods Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Code your solution to this interview question and run your code right in DataLemur's online SQL environment:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is confusing, you can find a detailed solution with hints here: Employee Salaries Higher Than Their Manager.
SQL Question 3: Why would it make sense to denormalize a database?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
For example, in a database that stores Flowers Foods sales analytics data, you might have separate tables for "customers," "orders," and "products," with foreign key constraints linking the tables together. This helps to ensure the integrity of the data and reduces redundancy, but it can also make queries that involve multiple tables more complex and slower to execute.
By de-normalizing the database, you can combine some of the data from separate tables into a single table, which can reduce the number of joins that are required to retrieve the data you need. This can speed up queries and improve the performance of your database.
However, it's important to carefully consider the trade-offs of de-normalization before making any changes to your database. De-normalization can make it more difficult to maintain the integrity and reliability of your data, and can also increase the risk of data redundancy. It's generally best to use de-normalization as a performance optimization technique only when necessary, and to carefully evaluate the benefits and drawbacks in the context of your specific database and workload.
SQL Question 4: Inventory Management and Sales Tracking
Flowers Foods needs a database to track its inventory of different types of baked goods and their sales. Design a database with a table, an table, and a table.
The table should contain columns for , , and . The table should correspond with the table and contain a , , and timestamp. The table should track the , , and the .
Example Input:
| product_id | product_name | product_type |
|---|---|---|
| 1 | White Sandwich Bread | Bread |
| 2 | 100% Whole Wheat Bread | Bread |
| 3 | Blueberry Muffins | Muffin |
| 4 | Chocolate Chip Cookies | Cookie |
Example Input:
| product_id | quantity | last_updated |
|---|---|---|
| 1 | 100 | 07/05/2022 08:00:00 |
| 2 | 250 | 07/05/2022 08:00:00 |
| 3 | 75 | 07/05/2022 08:00:00 |
| 4 | 150 | 07/05/2022 08:00:00 |
Example Input:
| product_id | quantity_sold | sales_date |
|---|---|---|
| 1 | 25 | 07/05/2022 10:00:00 |
| 2 | 35 | 07/05/2022 10:00:00 |
| 3 | 15 | 07/05/2022 10:00:00 |
| 4 | 20 | 07/05/2022 10:00:00 |
Answer:
This PostgreSQL query joins the , , and tables based on the and calculates the total quantity sold for each product. The result is a table with the product name, the current quantity in inventory, and the total quantity of each product sold. This can provide Flowers Foods with integral information on the success of their different products and aid in inventory management.
SQL Question 5: How does an inner join differ from a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
To demonstrate each kind, Imagine you were working on a Advertising Analytics project at Flowers Foods and had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
An retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
A retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 6: Average Sales Per Product
As a data analyst at "Flowers Foods", you're asked to calculate the average sales per product for the last month. It will help the marketing and sales teams to understand which products are performing well and make strategic decisions accordingly.
Your database has a table named with the following structure:
Example Input:
| sales_id | product_id | sale_date | quantity_sold |
|---|---|---|---|
| 101 | 1 | 07/15/2022 00:00:00 | 300 |
| 102 | 1 | 07/25/2022 00:00:00 | 500 |
| 103 | 2 | 07/05/2022 00:00:00 | 200 |
| 104 | 2 | 07/30/2022 00:00:00 | 450 |
| 105 | 3 | 07/21/2022 00:00:00 | 100 |
You need to compute the average sales for each product over this period of time.
Answer:
Your output should look like this:
Example Output:
| product_id | avg_quantity_sold |
|---|---|
| 1 | 400 |
| 2 | 325 |
| 3 | 100 |
On average, product 1 sold 400 units in the month of July, product 2 sold 325 units, and product 3 sold 100 units.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for identifying top-performing items or this Wayfair Y-on-Y Growth Rate Question which is similar for analyzing sales performance.
SQL Question 7: How would you go about optimizing a slow SQL query?
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. You might discover that your query is inefficient, or that there are many database writes at the same time you are doing a read, or maybe too many people are concurrently running queries on the same database server.
For Data Analyst and Data Science positions, knowing the ins-and-outs of SQL performance tuning is out-of-scope for the SQL interview round. However, knowing that joins are expensive, and indexes can speed up queries, is generally enough of an answer for Flowers Foods SQL interviews.
SQL Question 8: Evaluate Click-Through-Rate for Flowers Foods Advertisements
As an analyst in Flowers Foods, your department is running several online advertising campaigns to direct visitors toward your homepage and specific product pages. Uncertain of how well these campaigns are performing, the marketing manager has requested an analysis on the "Click-Through-Rate" (CTR), which is measured by the number of customers who clicked the ad divided by the number of times the ad was shown.
Given three tables:
Example Input:
| ad_id | view_date | user_id |
|---|---|---|
| 101 | 02/01/2022 | 7890 |
| 202 | 03/02/2022 | 4560 |
| 303 | 04/05/2022 | 2340 |
| 101 | 02/05/2022 | 8901 |
| 202 | 03/05/2022 | 5604 |
Example Input:
| ad_id | click_date | user_id |
|---|---|---|
| 101 | 02/03/2022 | 7890 |
| 202 | 03/04/2022 | 4560 |
| 303 | 04/06/2022 | 2340 |
| 101 | 02/06/2022 | 8901 |
| 202 | 03/06/2022 | 5604 |
Example Input:
| ad_id | ad_description |
|---|---|
| 101 | Spring Collection |
| 202 | Easter Specials |
| 303 | Mothers' Day Promo |
Calculate the Click-Through-Rate (CTR) for each advertisement, based on the data above.
Answer:
This SQL query joins the , , and tables using s on the field. The Click-Through-Rate is calculated as the count of click dates divided by the count of view dates for each advertisement. The function is used to convert the count number to a floating-point number to get a decimal output.
This gives us the click-through rate for each ad, allowing the company to understand which ads are performing well and which are not, which could influence future marketing decisions.
To solve a similar problem on DataLemur's free interactive SQL code editor, solve this Facebook SQL Interview question:
SQL Question 9: Find Customers Who Ordered Daisy Flowers
As an analyst for Flowers Foods, your task is to find all customers who have ordered Daisy flowers. The keyword 'Daisy' could appear anywhere in the 'product_name' field of our orders table.
Below are the provided tables for reference:
Example Input:
| order_id | customer_id | order_date | product_name |
|---|---|---|---|
| 1001 | 564 | 01/08/2022 | Daisy Bouquet |
| 1002 | 265 | 03/10/2022 | Rose Bouquet |
| 1003 | 223 | 05/18/2022 | Daisy and Tulip Mix |
| 1004 | 192 | 06/26/2022 | Daisy and Rose Mix |
| 1005 | 981 | 07/05/2022 | Sunflower Bouquet |
Example Output:
| customer_id |
|---|
| 564 |
| 223 |
| 192 |
Answer:
This SQL query will return all customer IDs where the 'product_name' field contains the word 'Daisy'. The '%' symbol on both sides of 'Daisy' inside the LIKE keyword is a wildcard character that matches any sequence of characters. Therefore, this query will match all records where 'Daisy' appears anywhere in the 'product_name' field.
SQL Question 10: 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.
Flowers Foods SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Flowers Foods SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the earlier Flowers Foods SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Google, Microsoft and Silicon Valley startups.
Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an online SQL coding environment so you can right in the browser run your query and have it checked.
To prep for the Flowers Foods SQL interview you can also be useful to solve interview questions from other food and facilities companies like:
- TreeHouse Foods SQL questions
- Darling Ingredients SQL questions
- Sanderson Farms SQL questions
- Monster Beverage SQL questions
But if your SQL query skills are weak, forget about diving straight into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers SQL topics like window functions and RANK vs. DENSE RANK – both of which come up often in Flowers Foods SQL interviews.
Flowers Foods Data Science Interview Tips
What Do Flowers Foods Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems to practice for the Flowers Foods Data Science Interview are:
- Experimental Design & AB Testing Questions
- Python or R Programming Questions
- Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Flowers Foods Data Science Interviews?
I believe the best way to prepare for Flowers Foods Data Science interviews is to read the book Ace the Data Science Interview.
It covers 201 interview questions sourced from Facebook, Google & startups. The book's also got a refresher on SQL, Product-Sense & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.
While the book is more technical in nature, it's also crucial to prepare for the Flowers Foods behavioral interview. A good place to start is by reading the company's culture and values.