11 PepsiCo SQL Interview Questions (Updated 2024)
At PepsiCo, SQL is used to analyze sales data to identify consumption patterns, helping them understand customer preferences and market trends. SQL also helps in managing inventory data, allowing them to improve supply chain processes and ensure that products are available when they are needed, this is why PepsiCo includes SQL coding questions in interviews for Data Science and Data Engineering positions.
To help you study for the PepsiCo SQL interview, here’s 11 PepsiCo SQL interview questions – can you solve them?
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11 PepsiCo SQL Interview Questions
SQL Question 1: Calculate Monthly Average Ratings for PepsiCo Products.
Assume you're a data analyst at PepsiCo. The company is interested in understanding the product rating trends on a monthly basis. Your task is to write a SQL query to calculate the monthly average rating for each PepsiCo product from the table, based on the column. In your analysis, you should extract month and year from and consider it while calculating the average.
The table includes the following columns:
- : The primary key of the table (int),
- : The customer who submitted the review (int),
- : The date when the review was submitted (date),
- : The PepsiCo product being reviewed (int),
- : The number of stars given by the customer (int, from 1 to 5).
To keep it simple, let's assume we have the following data in the table:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 100 | 2022-01-10 | 200 | 5 |
| 2 | 101 | 2022-01-15 | 201 | 4 |
| 3 | 102 | 2022-02-20 | 200 | 3 |
| 4 | 103 | 2022-02-25 | 201 | 5 |
| 5 | 104 | 2022-02-27 | 200 | 4 |
| 6 | 105 | 2022-03-01 | 201 | 5 |
| 7 | 106 | 2022-03-05 | 200 | 4 |
| 8 | 107 | 2022-03-10 | 200 | 3 |
| 9 | 108 | 2022-03-15 | 201 | 4 |
| 10 | 109 | 2022-04-01 | 200 | 5 |
Answer:
We can use the SQL window function over the partition of and . Order by is not required as we are not trying to create running totals or anything like that. The window function will allow us to calculate the monthly average ratings for each product. Here's a PostgreSQL query to achieve this:
This SQL script will return the , , , and the average rating of each product per month. We use to convert the column to numeric, so we can calculate the average correctly. Keep in mind that the function will return a value rounded to two decimal places by default.
To practice another window function question on DataLemur's free online SQL coding environment, try this Amazon SQL Interview Question:
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SQL Question 2: Department vs. Company Salary
Assume there was a table of PepsiCo employee salaries, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Solve this interview question directly within the browser on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution with hints here: Department vs. Company Salary.
SQL Question 3: Can you describe the meaning of a constraint in SQL in layman's terms?
Think of SQL constraints like the rules of a game. Just like a game needs rules to keep things fair and fun, a database needs constraints to keep things organized and accurate.
There are several types of SQL constraints like:
- NOT NULL: This constraint is like a bouncer at a nightclub - it won't let anything NULL through the door.
- UNIQUE: This constraint is like a VIP list - only special, one-of-a-kind values get in.
- PRIMARY KEY: This constraint is like an elected official - it's made up of NOT NULL and UNIQUE values and helps identify each row in the table.
- FOREIGN KEY: This constraint is like a diplomatic ambassador - it helps establish relationships between tables.
- CHECK: This constraint is like a referee - it makes sure everything follows the rules.
- DEFAULT: This constraint is like a backup plan - it provides a default value if no other value is specified.
So, whether you're playing a game or organizing a database, constraints are an important part of the process!
SQL Question 4: Designing a Database for Product Sales and Evaluating Performance for PepsiCo
As a Data Analyst at PepsiCo, your task is to design a database that maintains records of all products, their sales, and customer feedback.
Tables:
Table:
This table should include the (which is the primary key), , and the .
| product_id | product_name | product_type |
|---|---|---|
| P01 | Pepsi | Beverage |
| P02 | Lay's | Snack |
| P03 | Mountain Dew | Beverage |
| P04 | Doritos | Snack |
Table:
This table should include the (which is the primary key), , , and .
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| S01 | P01 | 06/20/2022 | 500 |
| S02 | P02 | 06/21/2022 | 300 |
| S03 | P01 | 06/22/2022 | 700 |
| S04 | P04 | 06/23/2022 | 400 |
Table:
This table should include (which is the primary key), , and .
| review_id | product_id | review_date | stars |
|---|---|---|---|
| R01 | P01 | 06/20/2022 | 4 |
| R02 | P02 | 06/21/2022 | 3 |
| R03 | P03 | 06/22/2022 | 5 |
| R04 | P04 | 06/23/2022 | 3 |
Your job is to write a SQL query that calculates the total number of sales of each product in the last month and their average star rating.
Answer:
This SQL query combines information from all the three tables using JOIN operations. For the sales made within the last month, it calculates the total quantity sold () and the average star rating () for each product. The results are grouped by the product's name and type.
The performance of the query can be improved by indexing the , columns, which are commonly used in the and clauses. The performance may also improve by partitioning the tables on , as it may have a limited number of distinct values.
SQL Question 5: How do you identify records in one table that are not present in a second table?
To discover records in one table that are not present in another, you can utilize a and filter out any values in the right-side table.
For example, say you had a table of PepsiCo customers and a 2nd table of all purchases made with PepsiCo. To find all customers who did not make a purchase, you'd use the following
:
This query returns all rows from the customers table, along with any matching rows from the purchases table. If there is no matching row in the purchases table, values will be returned for all of the right table's columns. The clause then filters out any rows where the purchases.id column is , leaving only customers who have not made a purchase.
SQL Question 6: Customer Order Analysis
As a data analyst at PepsiCo, you are asked to analyze customer orders for the year 2022, with a specific focus on customers who placed orders for more than 5 cases of any PepsiCo product in a single month. Please write an SQL query to extract all records of such customers from their database.
Here is a sample input and a corresponding output:
Example Input:
| order_id | customer_id | order_date | product_id | cases_ordered |
|---|---|---|---|---|
| 111 | C101 | 01/01/2022 | P5001 | 3 |
| 112 | C102 | 01/05/2022 | P5002 | 6 |
| 113 | C103 | 01/09/2022 | P5003 | 8 |
| 114 | C101 | 02/03/2022 | P5001 | 7 |
| 115 | C102 | 02/03/2022 | P5002 | 2 |
| 116 | C103 | 02/05/2022 | P5003 | 4 |
| 117 | C101 | 03/06/2022 | P5001 | 1 |
| 118 | C102 | 03/10/2022 | P5002 | 6 |
| 119 | C103 | 03/11/2022 | P5003 | 7 |
Example Output:
| month | customer_id | total_cases_ordered |
|---|---|---|
| 1 | C102 | 6 |
| 1 | C103 | 8 |
| 2 | C101 | 7 |
| 3 | C102 | 6 |
| 3 | C103 | 7 |
Answer:
The provided SQL query uses the function to get the and from the and the function to calculate the total number of cases ordered by each customer for each month. We filter the records for the year 2022 and where is more than 5. The result is then grouped by the and , to obtain the total cases ordered by each customer for that particular month.
SQL Question 7: Why is database normalization a good idea?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting PepsiCo's database to ever-changing business needs.
SQL Question 8: Find Average Annual Sales of Each Product
At PepsiCo, every individual product's annual sales are important to track and understand. For this question, your task is to calculate the average annual sales (in terms of units sold) of each product in the past 5 years.
Please consider the following sample table for :
Example Input:
| product_id | year | units_sold |
|---|---|---|
| 1 | 2016 | 30000 |
| 1 | 2017 | 40000 |
| 1 | 2018 | 45000 |
| 1 | 2019 | 50000 |
| 1 | 2020 | 55000 |
| 2 | 2016 | 15000 |
| 2 | 2017 | 25000 |
| 2 | 2018 | 35000 |
| 2 | 2019 | 35000 |
| 2 | 2020 | 20000 |
Answer:
You can use the function in SQL to solve this question:
This SQL statement groups all records by between the years 2016 and 2020, and then calculates the average units sold per year for each product. The function is then used to get the average annual sales for each product.
The expected output should be the product ids along with their average annual sales for the past 5 years.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating annual metrics or this Amazon Average Review Ratings Question which is similar for averaging values over a time period.
SQL Question 9: Click-through rates for PepsiCo's digital ads
Let's say that PepsiCo is analyzing the click-through rates of its digital advertising campaign. Each time an ad is viewed by a user, an entry is recorded in the table. If a user then clicks on the ad, an entry isrecorded in the table. The purpose of this analysis is to know the rate at which the viewers of the ad are actually clicking through.
The company wants you to write a SQL query to get the ad's click-through rate, i.e., number of ad clicks / number of ad views * 100 for each ad.
Here is some sample data for the and tables:
:
| view_id | user_id | view_date | ad_id |
|---|---|---|---|
| 101 | 1 | 06/08/2022 | 201 |
| 102 | 2 | 06/09/2022 | 202 |
| 103 | 3 | 06/10/2022 | 203 |
| 104 | 4 | 06/11/2022 | 204 |
| 105 | 5 | 06/12/2022 | 205 |
:
| click_id | user_id | click_date | ad_id |
|---|---|---|---|
| 501 | 1 | 06/08/2022 | 201 |
| 502 | 2 | 06/09/2022 | 202 |
| 503 | 1 | 06/10/2022 | 203 |
| 504 | 5 | 06/11/2022 | 205 |
Answer:
Given the tables and , the query works by first joining the tables on , which is the common attribute. The aggregate functions are then used to calculate the number of clicks and views for each ad. The query computes the click-through rate as the ratio of click counts to view counts and multiplies the result by 100 to get the rate in percentages. The final result is sorted in descending order of the click-through rate. The ensures all ads in the views table appear in the result, even those with no clicks.
To solve a similar problem about calculating rates, try this SQL interview question from TikTok within DataLemur's interactive SQL code editor:
SQL Question 10: What's the difference between the and operators?
is used to combine the output of multiple statements into one big result!
Suppose you were a Data Analyst at PepsiCo 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 .
SQL Question 11: Average PepsiCo Product Sales by Month and Region
On PepsiCo's database, there are two tables: and . The table records every sale made, including the , , and . The table has information about the in which each sale took place. Your task is to write a query that finds the average monthly sales for each product, in each region, in 2020.
Example Input:
| sale_id | product_id | sale_date | sale_amount | region_id |
|---|---|---|---|---|
| 101 | 1001 | 2020-01-01 | 150 | 1 |
| 102 | 1002 | 2020-02-15 | 220 | 2 |
| 103 | 1001 | 2020-02-20 | 180 | 1 |
| 104 | 1002 | 2020-03-10 | 200 | 1 |
| 105 | 1001 | 2020-03-15 | 160 | 2 |
Example Input:
| region_id | region_name |
|---|---|
| 1 | North |
| 2 | South |
Example Output:
| month | product_id | region_id | avg_sale |
|---|---|---|---|
| 1 | 1001 | 1 | 150.00 |
| 2 | 1001 | 1 | 180.00 |
| 2 | 1002 | 2 | 220.00 |
| 3 | 1001 | 2 | 160.00 |
| 3 | 1002 | 1 | 200.00 |
Answer:
This query first filters the table for transactions that took place in 2020. Then, it groups the rows by month, product ID, and region ID. The function calculates the average sales for each group.
Preparing For The PepsiCo SQL Interview
The best way to prepare for a PepsiCo SQL interview is to practice, practice, practice. In addition to solving the above PepsiCo SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each SQL question has hints to guide you, step-by-step solutions and crucially, there is an online SQL code editor so you can instantly run your query and have it checked.
To prep for the PepsiCo SQL interview you can also be a great idea to practice SQL problems from other food and facilities companies like:
- Archer Daniels Midland SQL questions
- Brown-Forman SQL questions
- Lamb Weston Holdings SQL questions
- Flowers Foods SQL questions
But if your SQL query skills are weak, forget about going right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers things like joining multiple tables and using ORDER BY – both of which show up often during SQL job interviews at PepsiCo.
PepsiCo Data Science Interview Tips
What Do PepsiCo Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems covered in the PepsiCo Data Science Interview include:
- AB Testing Questions
- Coding Questions in Python or R
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for PepsiCo Data Science Interviews?
I believe the optimal way to prep for PepsiCo Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It has 201 interview questions taken from Google, Microsoft & tech startups. The book's also got a refresher on Product Analytics, SQL & ML. And finally it's helped a TON of people, which is why it's got over 1000+ 5-star reviews on Amazon.
While the book is more technical in nature, it's also key to prepare for the PepsiCo behavioral interview. Start by understanding the company's unique cultural values.