At Seaboard, SQL is used to analyze shipping data, allowing them to pinpoint the most efficient routes and schedules for their marine logistics operations. They also rely on SQL to query databases for predictive modeling, helping them forecast demand and manage inventory more effectively, which is why, Seaboard asks SQL questions in interviews for Data Science, Data Engineering, and Data Analytics jobs.
Thus, to help you prep for the Seaboard SQL interview, we've collected 10 Seaboard SQL interview questions – can you solve them?
At Seaboard, we consider a customer as a 'whale' if they have spent in the top 1% within the last month. Can you write a query to identify these customers, their total expenditure in the last month and their average expenditure per transaction?
Assume you have access to the following tables:
customer_id | first_name | last_name |
---|---|---|
101 | Jon | Snow |
202 | Daenerys | Targaryen |
303 | Arya | Stark |
404 | Sansa | Stark |
505 | Tyrion | Lannister |
transaction_id | customer_id | purchase_date | amount |
---|---|---|---|
1001 | 101 | 2022-09-30 | 120.50 |
2002 | 101 | 2022-09-24 | 75.00 |
3003 | 202 | 2022-10-01 | 280.00 |
4004 | 202 | 2022-09-26 | 530.00 |
5005 | 303 | 2022-09-30 | 210.00 |
This SQL query first finds the total and average expenditure for each customer within the last month using a windowing function. Using that result, we calculate the 99th percentile of the total expenditure - this is our threshold for determining who is a 'whale' customer. The final query then selects the customers whose total expenditure is above this threshold. They are output along with their total and average expenditures.
To work on a similar customer analytics SQL question where you can code right in the browser and have your SQL solution automatically checked, try this Walmart SQL Interview Question:
Suppose there was a table of Seaboard employee salary data. Write a SQL query to find the 2nd highest salary among all employees.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Write a SQL query for this problem and run your code right in the browser:
You can find a step-by-step solution with hints here: 2nd Highest Salary.
Note: interviews at Seaboard often aren't trying to test you on a specific flavor of SQL. As such, you don't need to exactly know that is available in PostgreSQL and SQL Server, while is available in MySQL and Oracle – you just need to know the general concept!
Your answer should mention that the / operator is used to remove to return all rows from the first statement that are not returned by the second statement.
Here's a PostgreSQL example of using to find all of Seaboard's Facebook video ads with more than 50k views that aren't also being run on YouTube:
If you want to retain duplicates, you can use the operator instead of . The operator will return all rows, including duplicates.
Seaboard Corporation would like to analyze the performance of their products based on customer reviews. They are particularly interested in understanding the trends of customer satisfaction across different months.
Given a table containing , , , and (which indicates the rating given by the user for a particular product at a certain date), write a SQL query to calculate the average rating each product received for each month in 2022.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-01-18 | 50001 | 4 |
7802 | 265 | 2022-02-20 | 69852 | 4 |
5293 | 362 | 2022-03-25 | 50001 | 3 |
6352 | 192 | 2022-03-26 | 69852 | 3 |
4517 | 981 | 2022-02-15 | 69852 | 2 |
month | product_id | avg_rating |
---|---|---|
1 | 50001 | 4.00 |
2 | 69852 | 3.00 |
3 | 50001 | 3.00 |
3 | 69852 | 3.00 |
This query extracts the month from the using function, then groups the reviews by month and to calculate the average (rating) for each product per month. Additionally, we filter the query to only consider reviews submitted in the year 2022. The results are then sorted by and .
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
combines the results from two or more statements, but only returns the rows that are exactly the same between the two sets.
For example, let's use to find all of Seaboard's Facebook video ads with more than 10k views that are also being run on YouTube:
Seaboard is a global transportation company and deals with various shipping orders every day. The company wants to implement a SQL database to track all their shipping orders. The database should hold information about each shipping order, such as , , , , and .
Design the tables and their relationships in this database and write an SQL Query that will list all the customers who have at least one order that is yet to be shipped.
OrderID | CustomerID | OrderDate | ItemID | ShippingStatus |
---|---|---|---|---|
101 | 201 | 2022-09-01 | 301 | Shipped |
102 | 202 | 2022-09-02 | 302 | Not Shipped |
103 | 203 | 2022-09-03 | 303 | Shipped |
104 | 204 | 2022-09-11 | 304 | Not Shipped |
105 | 205 | 2022-09-15 | 305 | Not Shipped |
CustomerID | CustomerName |
---|---|
201 | John Doe |
202 | Jane Smith |
203 | Bill Gates |
204 | Elon Musk |
205 | Tim Cook |
To get this data, we will join the and tables on and filter where is 'Not Shipped'.
The above SQL query will return a list of the names of customers who have at least one order that has yet to be shipped. By using , we ensure that each customer name appears only once in the result set, even if they have multiple unshipped orders.
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:
Here’s your text with backticks added to SQL terms:
: This constraint is like a bouncer at a nightclub - it won't let anything through the door. : This constraint is like a VIP list - only special, one-of-a-kind values get in. : This constraint is like an elected official - it's made up of and values and helps identify each row in the table. : This constraint is like a diplomatic ambassador - it helps establish relationships between tables. : This constraint is like a referee - it makes sure everything follows the rules. : 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!
In Seaboard, a leading global shipping company, there are thousands of customers. Due to some internal policy, the company wants to filter all the customers who are from the USA and have made a shipment in the last 30 days. Also, ignore the customers who have a 'black mark' against their name in the company records. Can you write the SQL query to filter these customers?
Here's an example of how the customers table might look:
id | name | country | last_shipment_date | black_mark |
---|---|---|---|---|
1 | John Doe | USA | 2022-09-01 | No |
2 | Jane Smith | USA | 2022-08-20 | Yes |
3 | Mark Twain | UK | 2022-09-05 | No |
4 | Emily Johnson | USA | 2022-08-25 | No |
5 | Robert Brown | USA | 2022-09-04 | Yes |
6 | Oliver Davis | UK | 2022-07-30 | No |
7 | George Wilson | Canada | 2022-08-29 | No |
8 | Michael Jackson | USA | 2022-09-08 | No |
With this query, we are retrieving all columns of the records that belong to the customers from the USA who made a shipment in the last 30 days, excluding those who have a black mark. The clause is used to filter records, and the operator allows multiple conditions to be applied.
The date is chosen as '2022-09-01' because this date works as a reference point for "today" in this case. Depending on the database system, a current date command would be used in practice (i.e., CURRENT_DATE in PostgreSQL). In this case, the condition will check for all dates that are later than 30 days before the reference date.
Company Seaboard runs multiple digital ads leading to product pages on their website. When users click on these ads, they are taken to product pages where they can add the product to their cart. The click-through conversion rate is a key measure of the effectiveness of these ads.
Your task is to calculate the click-through conversion rate for the company. For each ad, this would be the number of user views of a product page that result in an added product to cart, divided by the total number of user views resulting from ad clicks.
Here are some sample data tables:
click_id | user_id | click_date | ad_id |
---|---|---|---|
101 | 789 | 2022-06-08 00:00:00 | 4001 |
102 | 754 | 2022-06-10 00:00:00 | 4002 |
103 | 321 | 2022-07-18 00:00:00 | 4001 |
104 | 654 | 2022-07-26 00:00:00 | 4002 |
105 | 987 | 2022-07-05 00:00:00 | 4003 |
view_id | user_id | view_date | ad_id | product_id |
---|---|---|---|---|
201 | 789 | 2022-06-08 00:00:00 | 4001 | 6001 |
202 | 321 | 2022-07-18 00:00:00 | 4001 | 6002 |
203 | 987 | 2022-07-05 00:00:00 | 4003 | 6003 |
activity_id | user_id | activity_date | product_id |
---|---|---|---|
301 | 789 | 2022-06-08 00:00:00 | 6001 |
302 | 321 | 2022-07-18 00:00:00 | 6002 |
This query first joins the , , and tables on and the relevant item identifiers. It counts the unique users who added a product to the cart and divides this by the unique users who clicked on an ad. This resulting rate provides the click-through conversion rate by ad.
To practice a related SQL problem on DataLemur's free interactive coding environment, try this Facebook SQL Interview question:
NULLs are NOT the same as zero or blank spaces in SQL. NULLs are used to represent a missing value or the abscence of a value, whereas zero and blank space are legitimate values.
It's important to handle NULLs carefully, because they can mess up your analysis very easily. For example, if you compare a NULL value using the = operator, the result will always be NULL (because just like Drake, nothing be dared compared to NULL). That's why many data analysis in SQL start with removing NULLs using the function.
The key to acing a Seaboard SQL interview is to practice, practice, and then practice some more! In addition to solving the above Seaboard SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each exercise has multiple hints, detailed solutions and most importantly, there's an interactive coding environment so you can easily right in the browser your SQL query answer and have it checked.
To prep for the Seaboard SQL interview it is also a great idea to practice SQL problems from other food and facilities companies like:
But if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers topics including aggregate window functions and filtering data with WHERE – both of which show up often during Seaboard interviews.
Besides SQL interview questions, the other types of questions to prepare for the Seaboard Data Science Interview include:
To prepare for Seaboard Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't forget about the behavioral interview – prepare for it with this list of common Data Scientist behavioral interview questions.