11 Boise Cascade SQL Interview Questions (Updated 2024)

Updated on

October 31, 2024

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 SQL Interview Questions

11 Boise Cascade SQL Interview Questions

SQL Question 1: Identify Top Customers of Boise Cascade

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.

Example Input:

order_idcustomer_idorder_dateproduct_idorder_volume
10122108/17/2021600012000
10231209/29/2021600021700
10322110/18/202160001200
10444111/24/202160003600
10531212/12/202160004800

Example Input:

customer_idcustomer_name
221Construction Co.
312Design Builders
441Urban Renovations

Answer:


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:

Microsoft SQL Interview Question: Super Cloud Customer

SQL Question 2: Employees Earning More Than Their Boss

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.

Boise Cascade Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

Example Output:

employee_idemployee_name
3Olivia 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:

Employees Earning More Than Their Manager

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 solution above is confusing, you can find a step-by-step solution with hints here: Highly-Paid Employees.

SQL Question 3: What's the main difference between ‘BETWEEN’ and ‘IN’ operators?

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 500and500 and 10k in spend, you could use :


To find ad campaigns that were run on Facebook and Google's Display Network, you could use :


Boise Cascade SQL Interview Questions

SQL Question 4: Average Monthly Lumber Sales

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:

Example Input:

sale_idsale_dateproduct_categoryunits_sold
100101/15/2022Lumber250
100201/20/2022Plywood300
100302/01/2022Lumber150
100402/25/2022Plywood400
100503/10/2022Lumber300
100603/20/2022Plywood200

You should output monthly averages of units sold by product category. In this case, your query should produce the following information:

Example Output:

monthproduct_categoryaverage_units_sold
1Lumber250.00
1Plywood300.00
2Lumber150.00
2Plywood400.00
3Lumber300.00
3Plywood200.00

Answer:

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:

Google SQL Interview Question

SQL Question 5: Can you give an example of a one-to-one relationship between two entities, vs. a one-to-many relationship?

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.

SQL Question 6: Filter Customer Orders Based on Delivery Status and Purchase Amount

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:

Example Input:

order_idcustomer_idorder_datepurchase_amountdelivery_status
100112304/14/202112000Delivered
100225607/18/20218000Delivered
100312402/20/202215000Processing
100498508/13/202111000Delivered
100578910/05/20219800Delivered

Answer:


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.

SQL Question 7: How does the RANK() window function differ from DENSE_RANK()?

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:

namedeals_closedrankdense_rank
Akash5011
Brittany5021
Carlos4032
Dave4043
Eve3053
Farhad1064

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.

SQL Question 8: Calculate the Click-Through-Rate (CTR) for Boise Cascade products

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:

Example Input:

ad_idproduct_iddatead_viewsad_clicks
201100106/01/2022800200
202100206/02/2022500150
203100306/01/20221000250
204100406/02/2022750225

Example Input:

cart_idad_idadd_to_cart
50120150
50220275
503203100
50420490

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.

Answer:


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:

Facebook App CTR SQL Interview question

SQL Question 9: Filter Customer Records with Specific Address Pattern

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:

Example Input:

customer_idnameemailaddress
1Micheal Smithmsmith@example.com6742 Gibson Street, Houston, TX, 77007
2Sarah Johnsonsjohnson@example.com8123 Central Avenue, Denver, CO, 80230
3John Williamsjwilliams@example.com111 Main Street, Austin, TX, 78701
4Felicity Brownfbrown@example.com726 Wood Road, Seattle, WA, 98109
5Alberto Garciaagarcia@example.com543 Side Street, Dallas, TX, 75001

Example Output:

customer_idnameemailaddress
1Micheal Smithmsmith@example.com6742 Gibson Street, Houston, TX, 77007
3John Williamsjwilliams@example.com111 Main Street, Austin, TX, 78701
5Alberto Garciaagarcia@example.com543 Side Street, Dallas, TX, 75001

Answer:


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.

SQL Question 10: What are SQL constraints, and can you give some examples?

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:

  • NOT NULL: This constraint ensures that certain columns, such as the employee's first and last name, cannot be .
  • UNIQUE: This constraint ensures that each employee has a unique employee ID.
  • PRIMARY KEY: This constraint combines the and constraints to create a primary key for the employee table. The primary key is a column or set of columns that uniquely identifies each row in the table.
  • FOREIGN KEY: This constraint establishes a relationship between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
  • DEFAULT: This constraint specifies a default value for a column in case no value is specified when a new row is inserted into the table.

SQL Question 11: Analyze Purchase History for Boise Cascade Customers

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 .

table:

customer_idfirst_namelast_namecitystate
001JohnDoeBoiseID
002JaneSmithTwin FallsID
003SamBrownCoeur d'AleneID
004AliceJohnsonPocatelloID
005BobDavisSun ValleyID

table:

purchase_idcustomer_idcategoryproductquantityprice_per_itempurchase_date
0001001lumber2x41002.52020-08-20
0002002lumber2x6503.52019-07-12
0003003plywood1/2"107.52020-01-15
0004001plywood3/4"208.02021-06-22
0005005lumber2x4802.52018-04-18

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?

Answer:

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:

Spotify JOIN SQL question

Boise Cascade SQL Interview Tips

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.

DataLemur SQL and Data Science Interview Questions

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.

DataLemur SQL Course

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.

Boise Cascade Data Science Interview Tips

What Do Boise Cascade Data Science Interviews Cover?

Beyond writing SQL queries, the other topics to prepare for the Boise Cascade Data Science Interview are:

  • Probability & Statistics Questions
  • Python or R Programming Questions
  • Data Case Study Questions
  • ML Modelling Questions
  • Behavioral Questions focussed on Boise Cascade cultural values

Boise Cascade Data Scientist

How To Prepare for Boise Cascade Data Science Interviews?

To prepare for Boise Cascade Data Science interviews read the book Ace the Data Science Interview because it's got:

  • 201 interview questions taken from tech companies like Netflix, Google, & Airbnb
  • a refresher on Python, SQL & ML
  • over 1000+ reviews on Amazon & 4.5-star rating

Acing Data Science Interview

Also focus on the behavioral interview – prep for it using this guide on behavioral interview questions.

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