At Boise Cascade, SQL is used to analyze lumber production efficiency, allowing them to identify areas for improvement. They also predict material pricing trends based on market data, helping them stay ahead in a competitive industry, for this reason, Boise Cascade asks SQL coding questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you practice, here’s 11 Boise Cascade SQL interview questions – can you answer each one?
Boise Cascade is a large manufacturer of plywood and engineered wood products in North America. The management wants to identify their VIP or whale customers. These are customers who have ordered the highest volume of products in the past year. They want to give these customers some promotional offers.
Can you write a SQL query to identify the top 10 customers who have ordered the most in terms of volume in the past year?
Please assume you are given database tables with the following structures.
order_id | customer_id | order_date | product_id | order_volume |
---|---|---|---|---|
101 | 221 | 08/17/2021 | 60001 | 2000 |
102 | 312 | 09/29/2021 | 60002 | 1700 |
103 | 221 | 10/18/2021 | 60001 | 200 |
104 | 441 | 11/24/2021 | 60003 | 600 |
105 | 312 | 12/12/2021 | 60004 | 800 |
customer_id | customer_name |
---|---|
221 | Construction Co. |
312 | Design Builders |
441 | Urban Renovations |
This query first joins the table with the table on . It then filters the entries to only include the records from the past year. It groups the records by each unique customer and sums up the for each customer. It then orders the results in descending order of the total order volume and restricts the results to the top 10.
To practice a related customer analytics question on DataLemur's free online SQL coding environment, try this Microsoft Azure Cloud SQL Interview Question:
Assume you had a table of Boise Cascade employee salaries. Write a SQL query to find the employees who earn more than their own manager.
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 |
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.
Try this question directly within the browser on DataLemur:
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 solution above is confusing, you can find a step-by-step solution with hints here: Highly-Paid Employees.
is used to select rows that match a range of values, whereas the operator checks for values in a specified list of values.
For example, say you were a Data Analyst at Boise Cascade and had a table of advertising campaign data.
To find campaigns with between 10k in spend, you could use :
To find ad campaigns that were run on Facebook and Google's Display Network, you could use :
As a business analyst at Boise Cascade, a leading manufacturer of plywood and engineered wood products in North America, you are tasked with analyzing sales data for the past year. Your specific task is to write a SQL query that calculates the average monthly sales quantity for each product category using a window function.
Consider a sales table as given below:
sale_id | sale_date | product_category | units_sold |
---|---|---|---|
1001 | 01/15/2022 | Lumber | 250 |
1002 | 01/20/2022 | Plywood | 300 |
1003 | 02/01/2022 | Lumber | 150 |
1004 | 02/25/2022 | Plywood | 400 |
1005 | 03/10/2022 | Lumber | 300 |
1006 | 03/20/2022 | Plywood | 200 |
You should output monthly averages of units sold by product category. In this case, your query should produce the following information:
month | product_category | average_units_sold |
---|---|---|
1 | Lumber | 250.00 |
1 | Plywood | 300.00 |
2 | Lumber | 150.00 |
2 | Plywood | 400.00 |
3 | Lumber | 300.00 |
3 | Plywood | 200.00 |
You can utilise the function with a window clause to calculate the average sales volumes per month, per product category. Your query might look something as follows:
In the above query, we first extract the month from . Then clause is used to divide the result set into partitions (or groups) based on the month and the product category. The window function is then applied to each of these partitions separately, calculating the average units sold for each product category per month. The clause arranges the output by month and then by product category.
To solve a related window function SQL problem on DataLemur's free interactive coding environment, solve this Google SQL Interview Question:
When designing a database schema, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a car and a license plate - each car has one license plate, and each license plate belongs to one car.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.
As an analyst at Boise Cascade, you're required to filter down the orders that have been 'Delivered' and have a purchase amount exceeding $10,000 within the past fiscal year. Write a query to accomplish this.
Let's consider a database named that contains the following columns:
order_id | customer_id | order_date | purchase_amount | delivery_status |
---|---|---|---|---|
1001 | 123 | 04/14/2021 | 12000 | Delivered |
1002 | 256 | 07/18/2021 | 8000 | Delivered |
1003 | 124 | 02/20/2022 | 15000 | Processing |
1004 | 985 | 08/13/2021 | 11000 | Delivered |
1005 | 789 | 10/05/2021 | 9800 | Delivered |
This SQL query filters down the records from the table where the delivery status is 'Delivered', the purchase amount is over $10,000, and the order date is within the past fiscal year. The clause filters for these conditions, with used to require all conditions to be met for a record to be included in the result set.
While both and are used to rank rows, the key difference is in how they deal with ties.
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
Suppose we had data on how many deals different salespeople at Boise Cascade:
To rank these salespeople, we could execute the following query:
The result of this query would be:
name | deals_closed | rank | dense_rank |
---|---|---|---|
Akash | 50 | 1 | 1 |
Brittany | 50 | 2 | 1 |
Carlos | 40 | 3 | 2 |
Dave | 40 | 4 | 3 |
Eve | 30 | 5 | 3 |
Farhad | 10 | 6 | 4 |
As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.
Consider a scenario where Boise Cascade, a large manufacturer of engineered wood products and plywood, conducts an online marketing campaign. They track the number of users who view their ads (), the number of users who click on their ads (), and subsequently the number of users who add their product to the cart via the ad (). The objective is to calculate the click-through rate (CTR) and click-to-cart-conversion rate.
This multi-step action tracking information is stored in two different tables:
ad_id | product_id | date | ad_views | ad_clicks |
---|---|---|---|---|
201 | 1001 | 06/01/2022 | 800 | 200 |
202 | 1002 | 06/02/2022 | 500 | 150 |
203 | 1003 | 06/01/2022 | 1000 | 250 |
204 | 1004 | 06/02/2022 | 750 | 225 |
cart_id | ad_id | add_to_cart |
---|---|---|
501 | 201 | 50 |
502 | 202 | 75 |
503 | 203 | 100 |
504 | 204 | 90 |
The task is to write a PostgreSQL query to compute the click-through-rate (CTR) as and the click-to-cart-conversion rate for each product and the respective date.
This PostgreSQL query first creates a common table expression by joining and on where is our left table. The query then calculates the CTR by dividing by , and the conversion rate by dividing by . It is important to cast the numbers as decimals to get accurate results since integer division could result in prematurely rounded outcomes. Lastly, the results are ordered by and .
To solve a related problem on DataLemur's free interactive coding environment, solve this SQL interview question asked by Facebook:
As a data analyst at Boise Cascade, you are tasked with identifying potential customers living in Texas (). The customer database has a combined field , where the state initials are embedded. the format of the address field is typically . Write a SQL query to extract all customer details whose address indicates they live in Texas.
Here is some example input and output for the problem:
customer_id | name | address | |
---|---|---|---|
1 | Micheal Smith | msmith@example.com | 6742 Gibson Street, Houston, TX, 77007 |
2 | Sarah Johnson | sjohnson@example.com | 8123 Central Avenue, Denver, CO, 80230 |
3 | John Williams | jwilliams@example.com | 111 Main Street, Austin, TX, 78701 |
4 | Felicity Brown | fbrown@example.com | 726 Wood Road, Seattle, WA, 98109 |
5 | Alberto Garcia | agarcia@example.com | 543 Side Street, Dallas, TX, 75001 |
customer_id | name | address | |
---|---|---|---|
1 | Micheal Smith | msmith@example.com | 6742 Gibson Street, Houston, TX, 77007 |
3 | John Williams | jwilliams@example.com | 111 Main Street, Austin, TX, 78701 |
5 | Alberto Garcia | agarcia@example.com | 543 Side Street, Dallas, TX, 75001 |
This query uses the SQL keyword to filter out the customer records where the address field contains . This pattern will match potential customers living in 'TX', Texas.
In a database, constraints are rules that the DBMS (database management system) follows when performing operations such as inserting, updating, or deleting data.
For example, consider a table of employee records at Boise Cascade. Some constraints that you might want to implement include:
Boise Cascade would like to understand the purchase behaviors of their customer segment. They are interested in knowing the total amount spent by each customer on each product category over the years.
Here are two tables - and .
customer_id | first_name | last_name | city | state |
---|---|---|---|---|
001 | John | Doe | Boise | ID |
002 | Jane | Smith | Twin Falls | ID |
003 | Sam | Brown | Coeur d'Alene | ID |
004 | Alice | Johnson | Pocatello | ID |
005 | Bob | Davis | Sun Valley | ID |
purchase_id | customer_id | category | product | quantity | price_per_item | purchase_date |
---|---|---|---|---|---|---|
0001 | 001 | lumber | 2x4 | 100 | 2.5 | 2020-08-20 |
0002 | 002 | lumber | 2x6 | 50 | 3.5 | 2019-07-12 |
0003 | 003 | plywood | 1/2" | 10 | 7.5 | 2020-01-15 |
0004 | 001 | plywood | 3/4" | 20 | 8.0 | 2021-06-22 |
0005 | 005 | lumber | 2x4 | 80 | 2.5 | 2018-04-18 |
Question: Could you write a SQL query to join these tables and calculate the total amount spent by each customer on each product category for every year?
In PostgreSQL, to solve this problem, you would first need to extract the year from the . Then, you can join the tables on and group by , and year.
This query gives a breakdown of total amount spent by each customer on each product category for each year. It uses the function to get the year from the and calculates the total spent as the product of and . Then it groups the result by , , , , and to provide a detailed analysis of Boise Cascade's customer purchase behaviors.
Because join questions come up frequently during SQL interviews, practice an interactive SQL join question from Spotify:
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the above Boise Cascade SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Meta, Google and industrial and electrical distribution companies like Boise Cascade.
Each DataLemur SQL question has multiple hints, detailed solutions and most importantly, there's an interactive SQL code editor so you can easily right in the browser your SQL query and have it checked.
To prep for the Boise Cascade SQL interview it is also a great idea to solve SQL questions from other industrial and electrical distribution companies like:
However, if your SQL foundations are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL interview tutorial.
This tutorial covers SQL concepts such as Union vs. UNION ALL and aggregate functions like SUM()/COUNT()/AVG() – both of which pop up frequently in Boise Cascade interviews.
Beyond writing SQL queries, the other topics to prepare for the Boise Cascade Data Science Interview are:
To prepare for Boise Cascade Data Science interviews read the book Ace the Data Science Interview because it's got:
Also focus on the behavioral interview – prep for it using this guide on behavioral interview questions.