8 Spectrum Brands SQL Interview Questions (Updated 2024)
At Spectrum Brands, SQL is used to analyze business trends, such as sales patterns and customer behavior for guided decision-making, as well as improve product performance forecasts by analyzing historical sales data, seasonal fluctuations, and market research for predicting future demand. For this reason, Spectrum Brands includes SQL coding questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
Thus, to help you prepare, we've collected 8 Spectrum Brands SQL interview questions – able to answer them all?
8 Spectrum Brands SQL Interview Questions
SQL Question 1: Calculate sales performance ranking per region.
Spectrum Brands is an international consumer products company, which sells products in various regions. You are asked to analyze the sales data in the last quarter and calculate the regional rankings based on the total sales amounts. Besides, provide a ranking within each region for different products.
Assume that we have a table that stores the sales information:
Example Input:
| sale_id | region | product_id | sale_date | quantity | unit_price |
|---|---|---|---|---|---|
| 1519 | 'North' | 104 | '06/20/2022' | 30 | 10.5 |
| 3665 | 'South' | 107 | '06/21/2022' | 15 | 20.0 |
| 5821 | 'East' | 104 | '06/22/2022' | 20 | 10.5 |
| 4891 | 'West' | 105 | '07/05/2022' | 25 | 5.6 |
| 7826 | 'North' | 106 | '07/10/2022' | 35 | 12.3 |
| 1578 | 'South' | 104 | '07/15/2022' | 40 | 10.5 |
The fields include:
- : An unique ID which presents each sale.
- : The region where the product is sold.
- : The ID of the product that was sold.
- : The date the product was sold.
- : The quantity of product that was sold.
- : The price of a unit product.
The output should contain a table of regions and product_id, ranked by their total sales within each region:
Example Output:
| region | product_id | total_sale | rank |
|---|---|---|---|
| 'North' | 106 | 429.5 | 1 |
| 'North' | 104 | 315.0 | 2 |
| 'South' | 104 | 420.0 | 1 |
| 'South' | 107 | 300.0 | 2 |
| 'East' | 104 | 210.0 | 1 |
| 'West' | 105 | 140.0 | 1 |
Answer:
This SQL query first calculates the total sales for each product in each region in the last quarter. It then ranks the products within each region based on the total sales. The window function is used to generate the rankings, and the clause is used to generate rankings within each region.
To practice another window function question on DataLemur's free interactive coding environment, solve this Google SQL Interview Question:
SQL Question 2: Employees Earning More Than Their Boss
Suppose you had a table of Spectrum Brands employee salaries. Write a SQL query to find the employees who earn more than their direct manager.
Spectrum Brands 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.
You can solve this interview question directly within the browser on DataLemur:
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 tough, you can find a detailed solution with hints here: Employees Earning More Than Managers.
SQL Question 3: What does do?
The COALESCE() function returns the first non-NULL value from a list of values. This function is often used to replace a NULL with some default value, so that you can then take a or of some column without NULLs messing things up.
For example, suppose you ran a customer satisfaction survey for Spectrum Brands and had statements like "I'd buy from Spectrum Brands again". In the survey, customers would then answer how strongly they agreed with a statement on a scale of 1 to 5 (strongly disagree, disagree, neutral, agreee, strongly agree).
Because attention spans are short, many customers skipped many of the questions, and thus our survey data might be filled with NULLs:
| customer_id | question_id | agree_scale |
|---|---|---|
| 101 | 1 | 4 |
| 101 | 2 | 5 |
| 202 | 1 | 4 |
| 202 | 2 | NULL |
| 303 | 1 | 5 |
| 303 | 2 | NULL |
Before doing further analytics on this customer survey data, you could replace the NULLs in the column with the value of (because that corresponds to the default 'neutral' answer) using the function:
This would result in the following:
| customer_id | question_id | agree_scale |
|---|---|---|
| 101 | 1 | 4 |
| 101 | 2 | 5 |
| 202 | 1 | 4 |
| 202 | 2 | 3 |
| 303 | 1 | 5 |
| 303 | 2 | 3 |
SQL Question 4: Filtering Customer Data based on Product Purchase History
As part of the marketing team at Spectrum Brands, you've been tasked with identifying a targeted group of customers for a new promotion for a specific product. Based on the customer database, write a SQL query to identify customers who have purchased the product 'ABC123' more than twice in the last six months. Also, these customers should not have returned the product or submitted any complaints about it.
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 001 | John | Doe | john.doe@gmail.com |
| 002 | Jane | Smith | jane.smith@gmail.com |
| 003 | Tom | Brown | tom.brown@gmail.com |
| 004 | Alice | Johnson | alice.johnson@gmail.com |
Example Input:
| order_id | customer_id | product_id | order_date |
|---|---|---|---|
| 601 | 001 | ABC123 | 02/01/2022 |
| 602 | 002 | XYZ456 | 03/01/2022 |
| 603 | 001 | ABC123 | 04/01/2022 |
| 604 | 003 | XYZ456 | 05/01/2022 |
| 605 | 001 | ABC123 | 06/01/2022 |
Example Input:
| return_id | order_id | return_date |
|---|---|---|
| 9001 | 602 | 03/02/2022 |
Example Output:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 001 | John | Doe | john.doe@gmail.com |
Answer:
The base query selects customers , , , and from the table. The filtering is based on the subquery, which fetches from the table where the product is 'ABC123', not returned, and ordered more than twice within the last six months. This subquery uses a LEFT JOIN to include all orders and match returns where they exist, excluding those rows where a return exists with . The date range for the order date checks if it's within the past six months using . The clause ensures this query only returns customers who purchased this product more than twice.
SQL Question 5: What's a stored procedure?
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For example, if you were a Data Analyst at Spectrum Brands working on a HR analytics project, you might create a stored procedure to calculate the average salary for a given department:
To call this stored procedure and find the average salary for the Data Analytics department you'd write the following query:
SQL Question 6: Click Through Conversion Rates for Spectrum Brands
You are a data analyst at Spectrum Brands, a company that sells a variety of consumer products, including home appliances, hardware, pet supplies, and home and garden items. They have a strong e-commerce platform, where they sell a significant portion of their products.
You have been tasked to study user interaction with their website and measure the effectiveness of their marketing efforts. One key metric you would like to study is the click-through conversion rate, defined as the number of users who added a product to cart after viewing it, divided by the total number of users who viewed the product.
You have access to two tables:
Example Input:
| activity_id | user_id | activity_type | product_id | activity_date |
|---|---|---|---|---|
| 10617 | 101 | view | 9820 | 07/25/2022 00:00:00 |
| 12505 | 102 | add_to_cart | 4450 | 08/11/2022 00:00:00 |
| 7392 | 103 | view | 9820 | 07/29/2022 00:00:00 |
| 9451 | 104 | view | 4450 | 08/14/2022 00:00:00 |
| 8519 | 104 | add_to_cart | 4450 | 08/14/2022 00:00:00 |
Example Input:
| product_id | product_name | category |
|---|---|---|
| 9820 | Spectrum Blender | Home Appliances |
| 4450 | SmartLock | Hardware |
You want to find the top 5 products with the highest click-through conversion rate in the month of August 2022.
Answer:
In the above query, we calculate the conversion rate as the ratio of distinct users who added an item to the cart to the distinct users who viewed the item for each product, specifically in the month of August 2022. We then order the products in decreasing order by conversion rate, and show only the top 5 products.
To practice a similar SQL interview question on DataLemur's free interactive coding environment, solve this SQL interview question asked by Facebook:
SQL Question 7: What are the similarities and differences between a clustered index and non-clustered index?
Clustered and non-clustered indexes are both used to improve query performance, but they differ in how they are implemented.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not. As a result, a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query efficiency & performance, a clustered index is generally faster for SELECT queries (reads) but updates (writes) to a clustered index are slower, as they require the data rows to be physically rearranged.
SQL Question 8: ANALYZING PURCHASES OF CUSTOMERS
For Spectrum Brands, a multinational consumer goods company, customer and sales data will provide invaluable insights for developing business strategies or making decisions. As an interviewee, you're asked to write a query that pulls information on the total amount spent by each customer on each type of product from the and tables.
Consider the following schema for two tables and :
Example Input:
| purchase_id | customer_id | product_id | purchase_date | amount |
|---|---|---|---|---|
| 1001 | 200 | 3001 | 02/25/2021 | 400 |
| 1002 | 201 | 3005 | 02/25/2021 | 650 |
| 1003 | 200 | 3001 | 05/25/2021 | 400 |
| 1004 | 202 | 3002 | 06/21/2021 | 200 |
| 1005 | 201 | 3001 | 09/25/2021 | 400 |
Example Input:
| product_id | product_type |
|---|---|
| 3001 | Walmart |
| 3002 | Amazon |
| 3003 | eBay |
| 3004 | BestBuy |
| 3005 | Costco |
Your task: Write a SQL query in PostgreSQL that joins these two tables and shows the total amount spent per customer per product type.
Answer:
This query first performs an inner join on and tables through the field. The aggregated function is used to calculate the total amount spent per customer () per product type (). clause groups the result by and . The output of this query will show the total amount of money each customer has spent on each type of product.
Since joins come up routinely during SQL interviews, practice this SQL join question from Spotify:
Spectrum Brands 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. Beyond just solving the earlier Spectrum Brands SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Google, Microsoft and Silicon Valley startups.
Each problem on DataLemur has hints to guide you, step-by-step solutions and best of all, there is an interactive coding environment so you can easily right in the browser your SQL query and have it graded.
To prep for the Spectrum Brands SQL interview you can also be a great idea to solve interview questions from other consumer good companies like:
- Energizer SQL questions
- Central Garden & Pet SQL questions
- Tempur Sealy SQL questions
- Leggett & Platt SQL questions
Dive into the latest news and updates from Spectrum Brands and stay informed about their business strategies!
In case your SQL coding skills are weak, forget about diving straight into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as creating pairs via SELF-JOINs and manipulating string/text data – both of which pop up often in Spectrum Brands interviews.
Spectrum Brands Data Science Interview Tips
What Do Spectrum Brands Data Science Interviews Cover?
Beyond writing SQL queries, the other topics covered in the Spectrum Brands Data Science Interview are:
- Probability & Stats Questions
- Coding Questions in Python or R
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Questions based on Spectrum Brands culture and values
How To Prepare for Spectrum Brands Data Science Interviews?
To prepare for Spectrum Brands Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from Facebook, Google & startups
- a refresher on SQL, Product-Sense & ML
- over 1000+ 5-star reviews on Amazon
Also focus on the behavioral interview – prep for that with this list of behavioral interview questions for Data Scientists.