Kellogg employees rely on SQL to analyze food processing data, which helps them identify areas for efficiency improvements in their production lines. They also use it to evaluate customer purchase patterns, allowing them to create marketing strategies that better connect with their audience, this is the reason why Kellogg includes SQL coding questions in interviews for Data Analytics, Data Science, and Data Engineering roles.
So, to help you prep, here’s 9 Kellogg SQL interview questions – able to solve them?
Company "Kellogg" is looking to identify their most valuable customers. These are defined as customers who purchase products frequently, especially their cereal products. Your task is to write a SQL query that lists out these top customers, who have made the most number of purchases in the last 1 year. You should return their user_id and the total number of purchases they have made.
The sales table keeps a record of all purchases. The schema for the sales table is as follows:
sale_id | user_id | sale_date | product_id | quantity |
---|---|---|---|---|
101 | 457 | 01/05/2021 | 60001 | 2 |
102 | 236 | 02/12/2021 | 60002 | 1 |
103 | 457 | 03/15/2021 | 60003 | 5 |
104 | 789 | 04/20/2021 | 60001 | 3 |
105 | 236 | 05/25/2021 | 60004 | 6 |
user_id | purchases |
---|---|
457 | 2 |
236 | 2 |
789 | 1 |
This query first filters out the sales transactions that occurred in the last 1 year. It then groups the transactions by user_id to count the number of transactions each customer has made. The result is ordered in descending order by the number of purchases to list the customers who made the most purchases first.
To solve a related customer analytics question on DataLemur's free online SQL code editor, try this Microsoft Teams Power User SQL Interview Question:
Given a table of Kellogg employee salaries, write a SQL query to find employees who make more money than their own manager.
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 |
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.
Solve this interview question and run your code right in DataLemur's online SQL environment:
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 tough, you can find a step-by-step solution with hints here: Well Paid Employees.
is used to combine the output of multiple statements into one big result!
Suppose you were a Data Analyst at Kellogg working on a Sales Analytics project, and had data on sales leads from both the company's Salesforce CRM and it's legacy Hubspot CRM. To write a query to analyze leads created before 2023 started, across both CRMs, you could use in the following way:
filters out duplicates, so each email/job title/company only shows up once (even if it's in both Salesforce and HubSpot CRMs).
On the other hand, the operator does NOT filter out duplicates, so if a lead shows up in both CRMs, it'll be listed twice in the output of .
As an analyst at Kellogg, you have been provided with a dataset of customer reviews on various Kellogg products. Each review includes the user's unique ID, the date the review was submitted, the product's unique ID, and the star rating (from 1 to 5) awarded by the user.
You're asked to write a SQL query that calculates the monthly average star rating for each product. The outcome should contain three columns: the month (in numbers from 1 to 12), the product ID, and the average star rating for that product in this month.
The information should be sorted by the month and product ids in ascending order.
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 |
Writeup: The above SQL query first uses the function to derive the month from the column. Then it groups the data by the extracted month and , in order to calculate the average of for each group using . Finally, it uses to sort the results in ascending order by the month first, then by the product id. The column names are selectively renamed to match the example output using .
mth | product | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
The function allows you to select the first non-NULL value from a list of values. It is often used to replace a NULL value with a default value in order to run aggregate functions like or on a column without being affected by values.
For example, if you conducted a customer satisfaction survey for a company and asked customers to rate their level of agreement with a statement on a scale of 1 to 10, some customers may not have answered all the questions, resulting in NULL values in the survey data:
customer_id | question_id | agree_amount |
---|---|---|
101 | 1 | 9 |
101 | 2 | 10 |
202 | 1 | 6 |
202 | 2 | NULL |
303 | 1 | 4 |
303 | 2 | NULL |
To replace the NULLs in the column with the default value of 5 which corresponds to neutral on the survey, you could run the following SQL query:
You'd get the following output:
customer_id | question_id | agree_amount |
---|---|---|
101 | 1 | 9 |
101 | 2 | 10 |
202 | 1 | 6 |
202 | 2 | 5 |
303 | 1 | 4 |
303 | 2 | 5 |
Sure, here is an SQL question on click-through rates:
Kellogg's company is running several digital ads for its different cereal products. For each ad, they measure the number of views and the number of clicks it gets. The click-through rate (CTR) is the ratio of users who clicked on a specific link to the number of total users who viewed the ad. CTR is a key performance indicator for their digital marketing campaigns.
Given the following table of ad data, write a SQL query to calculate the CTR for each product ad for the month of June and rank them in descending order by the CTR.
ad_id | product_name | views | clicks | date_shown |
---|---|---|---|---|
101 | Frosted Mini-Wheats | 3500 | 2000 | 06/05/2022 |
102 | Special K | 4000 | 1900 | 06/05/2022 |
103 | Raisin Bran | 3000 | 1350 | 06/18/2022 |
104 | Corn Pops | 2800 | 1200 | 06/18/2022 |
105 | Apple Jacks | 3800 | 1750 | 06/28/2022 |
The SQL query would look something like this:
This query first calculates the click-through rate for each ad by dividing the number of clicks by the number of views and multiplying by 100 to get a percentage. The clause is used to filter to only include ads that were shown in June (the 6th month). Finally, the results are ordered in descending order by the CTR, so the ad with the highest CTR will appear first.
To solve a similar problem about calculating rates, try this TikTok SQL Interview Question within DataLemur's online SQL code editor:
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
Unique Indexes are blazing fast. Non unique indexes can improve query performance, but they are often slower because of their non-unique nature.
Suppose you are given two tables - and . In the table, each row represents a customer with their , , and . In the table, each row represents a product purchased by a customer, with the , , and .
Given these two tables, the task is to write a SQL query that returns the first name, last name, and product name for all customers and their purchases that are located in the United States.
cust_id | first_name | last_name | country |
---|---|---|---|
100 | John | Doe | United States |
101 | Jane | Smith | Canada |
102 | Alice | Johnson | United States |
103 | Charlie | Brown | United Kingdom |
product_id | product_name | purchase_date | cust_id |
---|---|---|---|
2001 | Frosted Flakes | 2022-08-10 | 100 |
2002 | Raisin Bran | 2022-08-12 | 103 |
2003 | Corn Pops | 2022-08-15 | 102 |
2004 | Rice Krispies | 2022-08-25 | 100 |
2005 | Cocoa Krispies | 2022-08-30 | 101 |
This SQL query will create a join on the two tables using the field. It will then filter the results to only include customers located in the 'United States'. Finally, it will return the desired fields - , and .
Because join questions come up so often during SQL interviews, take a stab at this interactive Snapchat SQL Interview question using JOINS:
You are provided with two tables - and . Table contains information about the products including , , and .
The table contains information related to all the sales transactions including , , , .
Your task is to write a SQL query to calculate the normalized profit margin for each product every month. Normalized profit margin is calculated as the absolute value of ((Selling_Price - Production_Cost) / Selling_Price) * 100 then rounded to the nearest integer.
product_id | product_name | production_cost | selling_price |
---|---|---|---|
50001 | Corn Flakes | 1.5 | 3.5 |
69852 | Fruit Loops | 2 | 4 |
sale_id | product_id | sale_date | quantity_sold |
---|---|---|---|
6171 | 50001 | 06/08/2022 | 10 |
7802 | 69852 | 06/10/2022 | 15 |
5293 | 50001 | 06/18/2022 | 8 |
6352 | 69852 | 07/01/2022 | 12 |
Expected output is the product's profit margin for each month.
mth | product_name | normalized_profit_margin |
---|---|---|
6 | Corn Flakes | 57 |
6 | Fruit Loops | 50 |
7 | Fruit Loops | 50 |
Here, we are joining both tables on . After that, with the help of arithmetic operators, and functions, we calculate normalized profit margin. The resulting values are grouped by and .
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for handling sales and products data or this Wayfair Y-on-Y Growth Rate Question which is similar for dealing with sales data analysis.
The best way to prepare for a Kellogg SQL interview is to practice, practice, practice. Besides solving the earlier Kellogg SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each DataLemur SQL question has multiple hints, full answers and crucially, there's an interactive coding environment so you can right online code up your query and have it graded.
To prep for the Kellogg SQL interview you can also be a great idea to solve SQL problems from other food and facilities companies like:
However, if your SQL query skills are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers SQL topics like filtering data with WHERE and LEFT vs. RIGHT JOIN – both of these come up often in SQL interviews at Kellogg.
Besides SQL interview questions, the other types of problems to practice for the Kellogg Data Science Interview include:
To prepare for the Kellogg Data Science interview have a deep understanding of the company's values and company principles – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, prepare by reading Ace the Data Science Interview. The book's got: