8 SoftwareONE SQL Interview Questions (Updated 2024)
At SoftwareONE, SQL is often used for analyzing sales data to optimize software distribution, and retrieving client-specific information to personalize support services. For this reason SoftwareONE typically asks SQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
As such, to help you study for the SoftwareONE SQL interview, this blog covers 8 SoftwareONE SQL interview questions – can you solve them?
8 SoftwareONE SQL Interview Questions
SQL Question 1: Monthly Average Rating of each Product
Given a table 'reviews', write a SQL query to calculate the average number of stars each product received in each month.
Use the following table as input:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
The output should be formatted as follows:
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
The question requires to make use of the AVG window function to calculate the average rating each product received per month. To get the month from a timestamp, we can use the "EXTRACT" function in PostgreSQL.
This query works by first partitioning the data by product id and month and then calculating the average stars for each partition. The result is sorted by month and product id. Please note the use of the function to obtain the month from the .
To solve a related window function SQL problem on DataLemur's free interactive SQL code editor, solve this Amazon SQL Interview Question:
SQL Question 2: Find the average hours spent on each software application by employees
As a data analyst at SoftwareONE, you are asked to find out the average number of hours each employee spends on different software applications per month. The data is stored in two tables, the table which contains information about each employee, and the table which has logs of the employees' software application usage. Notably, the 'hours' column in the table represents the total number of hours an employee has used a particular software application in a month.
Example Input:
Example Input:
Answer:
This PostgreSQL query uses window function AVG() to calculate the average hours spent by each employee on each software in a specific month. The window PARTITION BY is used to divide the data into partitions based on employee id, software name, and month. It then calculates the average for each partition separately. The result is ordered by average_hours in descending order to get the software that each employee spends the most time on at the top.
To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for calculating time spent on different activities or this Microsoft Teams Power Users Question which is similar for finding top users based on usage.
SQL Question 3: List a few of the ways you find duplicate records in a table in SQL.
One way to find duplicate records in a table is by using , and then seeing which groups have more than one occurence:
Another way is by using the operator:
SQL Question 4: Calculate the total sales and total quantity sold grouped by product for SoftwareONE.
As a database administrator at SoftwareONE, you have been asked to analyze sales data. In order to better understand sales performances for the different products, you need to calculate the total sales and total quantity sold for each product.
The company uses two tables to store its sales data: & .
Example Input:
| product_id | name |
|---|---|
| 1 | Product A |
| 2 | Product B |
| 3 | Product C |
Example Input:
| sale_id | product_id | quantity_sold | sale_price |
|---|---|---|---|
| 100 | 1 | 5 | 150.00 |
| 101 | 2 | 3 | 200.00 |
| 102 | 1 | 2 | 100.00 |
The query should return the total price and total quantity sold for each product.
Example Output:
| product_name | total_sales | total_quantity_sold |
|---|---|---|
| Product A | 400.00 | 7 |
| Product B | 200.00 | 3 |
Answer:
To solve this problem, we will join the and tables on the field. We will then group by the and calculate the sum of and .
The result will then give us a table showing the total price and total quantity sold for each product, allowing the company to see how each product is performing in terms of sales.
SQL Question 5: How is a foreign key different from a primary key in a database?
To clarify the distinction between a primary key and a foreign key, let's examine employee data from SoftwareONE's HR database:
:
+------------+------------+------------+------------+ | employee_id| first_name | last_name | manager_id | +------------+------------+------------+------------+ | 1 | Aubrey | Graham | 3 | | 2 | Marshal | Mathers | 3 | | 3 | Dwayne | Carter | 4 | | 4 | Shawn | Carter | | +------------+------------+------------+------------+
In this table, serves as the primary key. It uniquely identifies each employee and cannot be null.
functions as a foreign key, linking to the of the employee's manager. This establishes a relationship between SoftwareONE employees and their managers, allowing for easy querying to find an employee's manager or see which employees report to a specific manager.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to connect each employee to their respective department and location.
SQL Question 6: Finding Customers with '@softwareone.com' in Email
You are given a database 'customers'. The table consists of various attributes such as customer_id, first_name, last_name, email, signup_date, among others. As a data analyst for SoftwareONE, you are asked to segment all customers whose email addresses end with '@softwareone.com'. Design a SQL query to filter these customers from the database.
Use the following sample data for reference:
Example Input:
| customer_id | first_name | last_name | signup_date | |
|---|---|---|---|---|
| 3245 | Joshua | Smith | joshua.smith@softwareone.com | 2021-03-10 |
| 6751 | Emma | Johnson | emma.johnson@gmail.com | 2021-05-14 |
| 3452 | Liam | Brown | liam.brown@softwareone.com | 2021-08-22 |
| 9472 | Olivia | Davis | olivia.davis@softwareone.com | 2021-10-01 |
| 7923 | Noah | Miller | noah.miller@yahoo.com | 2022-01-25 |
Answer:
You can use the keyword in SQL to filter these records. The percent sign '%' in the pattern is a wildcard character that can match any sequence of characters (including zero characters). The following SQL query does the job:
This query returns all records from the 'customers' table where the 'email' column ends with '@softwareone.com'. This can be useful for segmenting customers based on their email domain, and in this case, identifying customers who here at SoftwareONE.
SQL Question 7: What's the difference between a correlated and non-correlated sub-query?
A correlated sub-query is a sub-query that depends on the outer query and cannot be executed separately. It uses the outer query to filter or transform data by referencing a column from it, and the outer query uses the results of the inner query. In contrast, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data needed by the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all SoftwareONE customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Performance-wise, correlated sub-queries are generally slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 8: Calculate Square Root, Absolute difference and Round-off Operations
Suppose you are a data analyst at SoftwareONE and your task is to analyze the annual sales of the company. You have been given the data and you have to calculate the square root of total sales for each year, find the absolute difference in sales from the previous year and round off the sales to the nearest thousand for each year. Here's the table representing the data.
Example Input:
| year | total_sales |
|---|---|
| 2018 | 98750 |
| 2019 | 102800 |
| 2020 | 100450 |
| 2021 | 105600 |
Example Output:
| year | sqrt_sales | abs_diff_prev_year | rounded_sales |
|---|---|---|---|
| 2018 | 314.23 | NULL | 99000 |
| 2019 | 320.62 | 3050 | 103000 |
| 2020 | 317.07 | 2350 | 100000 |
| 2021 | 325.04 | 5150 | 106000 |
Answer:
This SQL query uses a combination of mathematical SQL functions. is used to get the square root of total sales, along with function is used to get the absolute difference in sales from the previous year, and is used to round off the sales to the nearest thousand. These calculations are performed per year in a chronological order determined by the clause.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for its use of sales data and window function or this Alibaba Compressed Mean Question which is similar for its use of mathematical computations and rounding off.
SoftwareONE 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 earlier SoftwareONE SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG tech companies and tech startups.
Each problem on DataLemur has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there is an interactive SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the SoftwareONE SQL interview you can also be useful to practice SQL questions from other tech companies like:
However, if your SQL foundations are weak, forget about diving straight into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers things like math functions and WHERE with AND/OR/NOT – both of which show up often in SQL interviews at SoftwareONE.
SoftwareONE Data Science Interview Tips
What Do SoftwareONE Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the SoftwareONE Data Science Interview are:
- Probability & Statistics Questions
- Python or R Programming Questions
- Product Analytics Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral Interview Questions
How To Prepare for SoftwareONE Data Science Interviews?
The best way to prepare for SoftwareONE Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
- A Crash Course on Python, SQL & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)