9 Kellogg SQL Interview Questions (Updated 2024)

Updated on

October 31, 2024

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?

Kellogg SQL Interview Questions

9 Kellogg SQL Interview Questions

SQL Question 1: Identify the Most Loyal Customers for Kellogg

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:

Example Input:

sale_iduser_idsale_dateproduct_idquantity
10145701/05/2021600012
10223602/12/2021600021
10345703/15/2021600035
10478904/20/2021600013
10523605/25/2021600046

Example Output:

user_idpurchases
4572
2362
7891

Answer:


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:

Microsoft SQL Interview Question: Teams Super User

SQL Question 2: Employees Earning More Than Their Boss

Given a table of Kellogg employee salaries, write a SQL query to find employees who make more money than their own manager.

Kellogg Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

Example Output:

employee_idemployee_name
3Olivia 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:

Employees Earning More Than Their Manager

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 tough, you can find a step-by-step solution with hints here: Well Paid Employees.

SQL Question 3: What sets UNION apart from UNION ALL?

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 .

Kellogg SQL Interview Questions

SQL Question 4: Analyze Product Review Trends

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.

Example Input:

review_iduser_idsubmit_dateproduct_idstars
61711232022-06-08500014
78022652022-06-10698524
52933622022-06-18500013
63521922022-07-26698523
45179812022-07-05698522

Answer:


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 .

Example Output:

mthproductavg_stars
6500013.50
6698524.00
7698522.50

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

DataLemur Window Function SQL Questions

SQL Question 5: What does do, and when would you use this function?

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_idquestion_idagree_amount
10119
101210
20216
2022NULL
30314
3032NULL

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_idquestion_idagree_amount
10119
101210
20216
20225
30314
30325

Sure, here is an SQL question on click-through rates:

SQL Question 6: Analyzing Click-Through Rates of Kellogg's Digital Ads

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.

Example Input:

ad_idproduct_nameviewsclicksdate_shown
101Frosted Mini-Wheats3500200006/05/2022
102Special K4000190006/05/2022
103Raisin Bran3000135006/18/2022
104Corn Pops2800120006/18/2022
105Apple Jacks3800175006/28/2022

Answer:

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: TikTok SQL question

SQL Question 7: What's the difference between a unique and non-unique index?

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.

SQL Question 8: Analyzing Customer Purchase and Product Information

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.

Sample Input:

cust_idfirst_namelast_namecountry
100JohnDoeUnited States
101JaneSmithCanada
102AliceJohnsonUnited States
103CharlieBrownUnited Kingdom

Sample Input:

product_idproduct_namepurchase_datecust_id
2001Frosted Flakes2022-08-10100
2002Raisin Bran2022-08-12103
2003Corn Pops2022-08-15102
2004Rice Krispies2022-08-25100
2005Cocoa Krispies2022-08-30101

Answer:


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:

Snapchat SQL Interview question using JOINS

SQL Question 9: Calculate Normalized Profit Margin of Cereal Products

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.

Example Input:

product_idproduct_nameproduction_costselling_price
50001Corn Flakes1.53.5
69852Fruit Loops24

Example Input:

sale_idproduct_idsale_datequantity_sold
61715000106/08/202210
78026985206/10/202215
52935000106/18/20228
63526985207/01/202212

Expected output is the product's profit margin for each month.

Example Output:

mthproduct_namenormalized_profit_margin
6Corn Flakes57
6Fruit Loops50
7Fruit Loops50

Answer:


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.

Kellogg SQL Interview Tips

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.

DataLemur Question Bank

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.

Free 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.

Kellogg Data Science Interview Tips

What Do Kellogg Data Science Interviews Cover?

Besides SQL interview questions, the other types of problems to practice for the Kellogg Data Science Interview include:

  • Probability & Statistics Questions
  • Coding Questions in Python or R
  • Open-Ended Data Case Studies
  • Machine Learning Questions
  • Behavioral Interview Questions

Kellogg Data Scientist

How To Prepare for Kellogg Data Science Interviews?

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:

  • 201 Interview Questions from FAANG, tech startups, and Wall Street
  • A Refresher covering Python, SQL & ML
  • Amazing Reviews (1000+ 5-star reviews on Amazon)

Acing Data Science Interview

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