11 J.M. Smucker SQL Interview Questions (Updated 2024)
At J.M. Smucker, SQL is used for analyzing sales data to track key performance indicators, helping the company understand how well their products are doing in the market. They also rely on SQL for managing customer databases, which supports targeted marketing campaigns, helping them connect with consumers effectively, that is why J.M. Smucker frequently includes SQL problems in their interviews for Data Science, Analytics, and Data Engineering roles.
Thus, to help you prep, here’s 11 J.M. Smucker SQL interview questions – able to answer them all?
11 J.M. Smucker SQL Interview Questions
SQL Question 1: Identify the Top-purchasing Customers for J.M. Smucker
In J.M. Smucker, a "whale" customer could be classified as a user who purchases a large amount of their products frequently. Your task is to identify these top-purchasing customers from their sales database. To make this a bit challenging, imagine that the database contains wild variations of entries due to large-scale promotions, product launches, or festivals. Your goal will be to smooth out these variations by calculating the average purchases made by each user over the last 6 months.
Example Input:
| order_id | customer_id | purchase_date | product_id | units_purchased |
|---|---|---|---|---|
| 9123 | 456 | 06/08/2022 | 10234 | 6 |
| 8745 | 789 | 06/15/2022 | 12345 | 3 |
| 6045 | 456 | 06/18/2022 | 12345 | 9 |
| 8156 | 321 | 07/20/2022 | 10234 | 4 |
| 7136 | 987 | 07/30/2022 | 12345 | 8 |
Example Input:
| product_id | product_name |
|---|---|
| 10234 | Strawberry Jam |
| 12345 | Grape Jelly |
Example Output:
| customer_id | avg_units_purchased |
|---|---|
| 456 | 7.5 |
| 789 | 3.0 |
| 321 | 4.0 |
| 987 | 8.0 |
Answer:
The above query first computes the average units purchased per month for each customer for the most recent 6 months. The outer query then calculates the average of these monthly averages to get the overall average for each customer over the last 6 months. The clause sorts the customers in descending order of units purchased on average, putting the "whale" customers first.
To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL solution instantly executed, try this Walmart SQL Interview Question:
Check out J.M. Smucker's news and stories to learn about their recent developments and commitment to quality in the food sector! Keeping up with Smucker's updates can help you understand how they maintain their reputation while adapting to market changes.
SQL Question 2: Employees Earning More Than Their Boss
Imagine you had a table of J.M. Smucker employee salaries. Write a SQL query to find all employees who earn more than their own manager.
J.M. Smucker 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.
Write a SQL query for this 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 here: Highly-Paid Employees.
SQL Question 3: Could you explain what a self-join is and provide examples of when it might be used?
A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the clause, and give each instance of the table a different alias. You can then join the two instances of the table using a clause, and use a clause to specify the relationship between the rows.
For example, say you were doing an HR analytics project and needed to analyze how much all J.M. Smucker employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of J.M. Smucker employees who work in the same department:
This query returns all pairs of J.M. Smucker employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same J.M. Smucker employee being paired with themselves).
SQL Question 4: Calculate Monthly Average Ratings
As a business analyst for J.M. Smucker, a company known for producing fruit spreads, peanut butter, and other food products, you have been tasked with analyzing the product reviews from customers. Product management wants to know the average rating of each product on a monthly basis.
Tables provided:
:
| product_id | product_name |
|---|---|
| 50001 | Smucker's Fruit Spread |
| 69852 | Jif Peanut Butter |
:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Write a SQL query that returns the average monthly ratings for each product.
Answer:
This query begins with a join to connect and tables using the , which is common in both tables. The extracts the month from the date of submission and calculates the average stars or ratings for that month. clause groups the results by month and product name, and clause sorts the output by month in ascending and average stars in descending order. The result is a table showing the monthly average ratings for each product.
To solve a similar window function question on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
SQL Question 5: Consider unique indexes, and non-unique indexes. What are some similarities and differences?
While both types of indexes improve the performance of SQL queries by providing a faster way to lookup rows of data, a unique index enforces the uniqueness of the indexed columns while a non-unique index allows duplicate values in the indexed columns.
Suppose you had a table of J.M. Smucker employees. Here's an example of a unique index on the column:
This index would ensure that no two J.M. Smucker employees have the same , which could be used as a unique identifier for each employee.
Here's a non-unique index example example on the column:
This index would not enforce uniqueness, but it could be used to improve the performance of queries that filter or sort the data based on the column. For example, if you want to quicklly retreive all Data Scientists, the database can use the index to efficiently locate and retrieve the desired records without having to do a full table scan on all J.M. Smucker employees.
SQL Question 6: Analyzing Customer Purchase Patterns
The J.M. Smucker Company wishes to understand the purchasing habits of its customers better. They want a SQL query that can filter their database to identify customers who have purchased both pet food and coffee products in the year 2022.
The table has the following format:
Example Input:
| product_id | product_type |
|---|---|
| 123 | Pet Food |
| 456 | Coffee |
| 789 | Pet Food |
| 321 | Coffee |
And the table has the following format:
Example Input:
| purchase_id | customer_id | product_id | purchase_date |
|---|---|---|---|
| 1 | 200 | 123 | 02/03/2022 |
| 2 | 100 | 321 | 01/05/2022 |
| 3 | 200 | 456 | 03/06/2022 |
| 4 | 300 | 789 | 05/01/2022 |
| 5 | 100 | 789 | 04/02/2022 |
| 6 | 200 | 321 | 07/10/2022 |
Answer:
In this PostgreSQL query, we first join the and tables together based on the . We then filter the purchases to just those where the is either 'Pet Food' or 'Coffee' and the year of purchase is 2022. Finally, we group the filtered purchases by the , and in our clause, we ensure that we only keep the customers who have purchased two distinct product types (i.e., 'Pet Food' and 'Coffee'). This gives us a list of customers who have purchased both these product types in the year 2022.
SQL Question 7: How do foreign and primary keys differ?
To better understand the difference between a primary key and a foreign key, let's use an example from J.M. Smucker's marketing analytics database, which holds data on Google Ads campaigns:
:
| ad_id | campaign_id | keyword | click_count |
|---|---|---|---|
| 1 | 100 | J.M. Smucker pricing | 10 |
| 2 | 100 | J.M. Smucker reviews | 15 |
| 3 | 101 | J.M. Smucker alternatives | 7 |
| 4 | 101 | buy J.M. Smucker | 12 |
In this table, serves as the primary key. It uniquely identifies each ad and cannot be null.
is a foreign key that connects to the of the corresponding Google Ads campaign. This establishes a relationship between the ads and their campaigns, enabling easy querying to find which ads belong to a specific campaign or which campaigns a specific ad belongs to.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to link each ad to its ad group and the Google Ads account that the campaigns belong to, respectively.
SQL Question 8: Calculate the Average Sales Per Product
With J.M.Smucker being a famous food processing company, a possible interview question could be about calculating the average sales per product for each month of the year.
For example:
"In the company J.M. Smucker, they often deal with different quantities of products sold every month. Write a SQL query to find the average quantity of each product sold per month."
Given the following data:
Example Input:
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| 1023 | 1001 | 06/12/2022 | 150 |
| 1390 | 2002 | 06/15/2022 | 180 |
| 1729 | 1001 | 06/25/2022 | 270 |
| 2350 | 2002 | 07/14/2022 | 220 |
| 2593 | 1001 | 07/18/2022 | 360 |
Calculate the average demand for each product for each month.
Example Output:
| mth | product | avg_quantity |
|---|---|---|
| 6 | 1001 | 210 |
| 6 | 2002 | 180 |
| 7 | 2002 | 220 |
| 7 | 1001 | 360 |
Answer:
The PostgreSQL query to solve this would be:
The function is used to obtain the month from the date of the sales. The query then groups the results by both the month and the product, and calculates the average quantity sold for each grouping using the function.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating sales rate or this Amazon Average Review Ratings Question which is similar for data grouped by month.
SQL Question 9: Find Customers Buying Strawberry Products
The J.M. Smucker Company would like to identify customers who have a preference for strawberry products. Each product has a name that includes the product's flavor. Design a SQL query that fetches the customer_id and product_name from customer purchases where the flavour is strawberry. Assume you have access to the tables and .
Sample data:
Table:
| customer_id | name | |
|---|---|---|
| 1 | John Dow | johndow@email.com |
| 2 | Jane Doe | janedoe@email.com |
| 3 | Alice Smith | alicesmith@email.com |
Table:
| purchase_id | customer_id | product_name | purchase_date |
|---|---|---|---|
| 1001 | 1 | Strawberry Jam | 2022-06-01 |
| 1002 | 2 | Blueberry Jam | 2022-06-03 |
| 1003 | 1 | Strawberry Jelly | 2022-06-05 |
| 1004 | 3 | Grape Jam | 2022-06-07 |
| 1005 | 2 | Strawberry Syrup | 2022-06-09 |
Answer:
This query first joins the and tables on the field. It then uses the keyword to filter for only rows where the includes the word 'Strawberry'. The '%' is a wildcard character that matches any sequence of characters. Thus, '%Strawberry%' will match any that contains 'Strawberry' anywhere in it.
SQL Question 10: What does the constraint do?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
Say for example you had sales analytics data from J.M. Smucker's CRM (customer-relationship management) tool.
The constraint ensures that the data in the field of the table is valid, and prevents the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and can be used to ensure that data is not deleted from the accounts table if there are still references to it in the table.
SQL Question 11: Combine and Analyze Customer and Sales Data
As a data analyst in J.M. Smucker, you are given two tables. One table contains details about the customers () and another table contains details about the sales ().
Your task is to write a SQL query that joins the and tables, and provides a summary of total sales quantity and total spend for every customer. Provide the results sorted by total spend from high to low.
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 1 | John | Doe | john.doe@example.com |
| 2 | Jane | Doe | jane.doe@example.com |
| 3 | Bob | Smith | bob.smith@example.com |
Example Input:
| sale_id | customer_id | Product_name | quantity | price |
|---|---|---|---|---|
| 1 | 1 | Jam | 2 | 5.00 |
| 2 | 1 | Peanut Butter | 1 | 2.50 |
| 3 | 2 | Coffee | 1 | 10.00 |
| 4 | 3 | Jam | 1 | 5.00 |
| 5 | 3 | Peanut Butter | 2 | 2.50 |
Answer:
This PostgreSQL query first combines the two tables: and , using an inner join with the customer's ID. The function is used to compute the total quantity of products and the total spending by each customer. Finally, the result is grouped by customer details (first name, last name, and email), and ordered by total spend in descending order. This provides a detailed view of each customer's purchasing behavior, and can be used by J.M. Smucker to analyze sales and tailor promotions and marketing.
Since join questions come up routinely during SQL interviews, try an interactive SQL join question from Spotify:
How To Prepare for the J.M. Smucker SQL Interview
The key to acing a J.M. Smucker SQL interview is to practice, practice, and then practice some more! Besides solving the above J.M. Smucker SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Amazon, JP Morgan, and food and facilities companies like J.M. Smucker.
Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an online SQL code editor so you can right online code up your SQL query and have it graded.
To prep for the J.M. Smucker SQL interview it is also a great idea to solve SQL problems from other food and facilities companies like:
- Campbell Soup SQL questions
- Constellation Brands SQL questions
- Hershey SQL questions
- Seaboard SQL questions
In case your SQL coding skills are weak, don't worry about going right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers SQL topics like math functions like ROUND()/CEIL() and RANK() window functions – both of which come up often during J.M. Smucker SQL assessments.
J.M. Smucker Data Science Interview Tips
What Do J.M. Smucker Data Science Interviews Cover?
Besides SQL interview questions, the other topics to practice for the J.M. Smucker Data Science Interview include:
- Experimental Design & AB Testing Questions
- Coding Questions in Python or R
- Product & Business Analytics Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for J.M. Smucker Data Science Interviews?
To prepare for the J.M. Smucker Data Science interview make sure you have a firm understanding of the company's values and company principles – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Google, Microsoft & tech startups
- A Refresher on Python, SQL & ML
- Great Reviews (1000+ reviews, 4.5-star rating)