Andersons employees rely on SQL to analyze customer purchase patterns, helping them see which products are selling quickly and which ones might need more attention. They also use SQL to predict future sales trends, focusing on their products and seasonal influences, helping them stay ahead of market demands, which is why Andersons often tests SQL problems in interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you prepare for the Andersons SQL interview, we've curated 9 Andersons SQL interview questions in this blog.
Andersons Inc sells a variety of products which attract many customer reviews. Each review includes information about the product, the star rating given by the customer, and the date the review was submitted. Your task is to write a SQL query to determine the monthly average rating for each product. The output should be ordered by the month and then by the product ID. Assume that month is represented as an integer where January is 1 and December is 12.
Let's use the table with each row representing a review:
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2020-06-08 | 50001 | 4 |
7802 | 265 | 2020-06-10 | 69852 | 4 |
5293 | 362 | 2020-06-18 | 50001 | 3 |
6352 | 192 | 2020-07-26 | 69852 | 3 |
4517 | 981 | 2020-07-05 | 69852 | 2 |
Here's the SQL query in PostgreSQL that can solve this problem:
This SQL query first extracts the month from each review's with the function. It then calculates the average star rating for each product for each month with the function. The clause is used to separate the records into groups by month and product, and the clause is used to order the records by month and then by product. The result of the function is then cast as a decimal with 2 places after the decimal with the clause, to display the ratings with precision.
To solve a similar window function question on DataLemur's free online SQL code editor, try this Google SQL Interview Question:
Given a table of Andersons employee salaries, write a SQL query to find the 2nd highest salary at the company.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Solve this interview question and run your code right in the browser:
You can find a step-by-step solution here: 2nd Highest Salary.
In database schema design, a one-to-one relationship is when each entity is associated with only one instance of the other. For instance, a US citizen's relationship with their social-security number (SSN) is one-to-one because each citizen can only have one SSN, and each SSN belongs to one person.
A one-to-many relationship, on the other hand, is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a person and their email addresses - one person can have multiple email addresses, but each email address only belongs to one person.
Andersons is a company that sells variety of products. The firm is interested in knowing which product has been most popular among customers. They define popularity in terms of the number of units sold. Your task is to design a solution to determine the most popular product based on data from two tables: and . Also, consider when the same product is sold in multiple transactions, each transaction needs to be accounted for separately in the total units sold.
Given two tables: and
product_id | product_name | product_price |
---|---|---|
1 | Book | 10 |
2 | Pencil | 2 |
3 | Notebook | 5 |
4 | Bag | 35 |
5 | Calculator | 15 |
sale_id | product_id | product_units | sale_date |
---|---|---|---|
1 | 1 | 100 | 06/10/2022 |
2 | 1 | 50 | 07/15/2022 |
3 | 2 | 200 | 06/12/2022 |
4 | 2 | 150 | 07/18/2022 |
5 | 3 | 50 | 06/18/2022 |
6 | 4 | 20 | 07/20/2022 |
7 | 5 | 30 | 06/25/2022 |
8 | 5 | 50 | 07/25/2022 |
Your goal is to run a query that will produce a table in the below format:
product_name | total_units_sold |
---|---|
???? | ??? |
A possible query to solve this problem can be:
This query first joins the and tables on the column. Then it groups the results by and calculates the total units sold for each product using the function. The results are then ordered in decreasing order of , and only the top result (i.e., the product with the most units sold) is returned.
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 Andersons SQL interviews.
Anderson's is an online retail store that sells a wide range of products across the United States. They have a large customer database and are interested in segmenting their customers to design targeted marketing campaigns.
Specifically, they are interested in the following segments of their customer base:
Customers who have made a purchase in the last 30 days, live in California (CA), and have made at least one purchase of over $100.
Customers who have not made a purchase in the last 60 days and live outside of California (CA).
Using the and tables below, write a SQL query to identify the customers for each segment.
customer_id | state |
---|---|
101 | CA |
102 | NY |
103 | CA |
104 | TX |
105 | NV |
order_id | customer_id | purchase_date | amount |
---|---|---|---|
301 | 101 | 2022-09-30 | 120 |
302 | 102 | 2022-08-20 | 70 |
303 | 103 | 2022-10-02 | 130 |
304 | 104 | 2022-09-20 | 80 |
305 | 105 | 2022-07-15 | 200 |
For the first segment:
For the second segment:
The above queries return a list of and for each of the identified segments. For the first segment, we select customers who made a purchase within the last 30 days, live in California, and where their purchase amount was over $100. For the second segment, the query retrieves customers who have not made a purchase in the last 60 days and live outside of California. Notice how we use a to ensure we include all customers even if they don't have corresponding rows in the table.
The function is used to remove NULLs, and replace them with another value.
For example, say you were a Data Analyst at Andersons and were doing a customer analytics project to see who the most marketing-engaged customers were, and had access to the below table.
customer_id | email_engagement | sms_engagement |
---|---|---|
101 | very_active | not_opted_in |
201 | un-subscribed | NULL |
301 | NULL | not_opted_in |
401 | not_active | very_active |
501 | very_active | mildly_active |
303 | NULL | NULL |
Before you could procede, you had to remove the NULLs, and replace them with the default value ( for email, and for SMS). To do this, you'd run the following query:
This would get you the following output:
customer_id | email_engagement | sms_engagement |
---|---|---|
101 | very_active | not_opted_in |
201 | un-subscribed | not_opted_in |
301 | not_active | not_opted_in |
401 | not_active | very_active |
501 | very_active | mildly_active |
303 | not_active | not_opted_in |
Andersons, a corporate firm with a large number of employees, has tasked you with keeping track of their employee salaries. Given a database table with employee names, their department and their respective salaries, find the average salary for each department.
Let's call the table . Here is an example:
emp_id | emp_name | dept_name | salary |
---|---|---|---|
1 | John | Human Resources | 5000 |
2 | Doe | Marketing | 7500 |
3 | Emily | Sales | 8000 |
4 | Rob | Human Resources | 6000 |
5 | Jane | Marketing | 8500 |
6 | Rick | Sales | 9000 |
7 | Elliot | Human Resources | 5500 |
Now, your task for this interview question is to compute the average salary for each department.
This SQL query uses the function to compute the average salary for each department. The clause groups the salary data by their respective department. The result gives us the department's name and the corresponding average salary.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average values grouped by categories, or this Alibaba Compressed Mean Question which is similar for aggregating and deriving statistics from data.
You are tasked with analyzing Andersons' customer database. You find that information about customers is spread across two tables: and . The table contains customer-specific information like , , , and . The table contains the details of each order made by customers, such as , , , and as a foreign key.
The goal is to write a SQL query as per below requirements:
Here are sample tables and to illustrate the problem:
customer_id | first_name | last_name | |
---|---|---|---|
1 | Eleni | Clark | eleni@clark.com |
2 | John | Doe | john@doe.com |
3 | Jane | Smith | jane@smith.com |
order_id | order_date | product_id | customer_id |
---|---|---|---|
101 | 07/28/2022 | 59 | 1 |
102 | 07/28/2022 | 72 | 1 |
103 | 07/28/2022 | 18 | 2 |
This PostgreSQL query uses an to combine the and tables. The condition indicates that we want to combine rows where the is the same in both tables. The columns in the clause are the ones we want to display: the customer's name and email, and the associated and from the table.
Because join questions come up routinely during SQL interviews, take a stab at this interactive Snapchat JOIN SQL interview question:
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Andersons SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Andersons SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like FAANG tech companies and tech startups.
Each exercise has multiple hints, detailed solutions and best of all, there's an online SQL code editor so you can instantly run your query and have it graded.
To prep for the Andersons SQL interview you can also be helpful to solve SQL questions from other food and facilities companies like:
But if your SQL foundations are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers topics including HAVING and transforming strings with CONCAT()/LOWER()/TRIM() – both of these show up often in Andersons interviews.
Beyond writing SQL queries, the other question categories covered in the Andersons Data Science Interview are:
To prepare for the Andersons Data Science interview have a strong understanding of the company's cultural values – 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: