10 Asseco SQL Interview Questions (Updated 2024)
Asseco employees use SQL often for analyzing customer data for personalized software solutions and managing database systems for efficient data storage and retrieval. Unsurprisingly this is why Asseco frequently asks SQL coding questions in interviews for Data Analyst, Data Science, and BI jobs.
As such, to help you ace the Asseco SQL interview, here's 10 Asseco SQL interview questions in this article.
10 Asseco SQL Interview Questions
SQL Question 1: Find the top "Whale" Users for Asseco
Asseco is interested in identifying their top "Whale" users. The term "Whale" is used in business to imply customers who are extraordinarily profitable, usually because of their high-frequency activity. In Asseco's case, their top users are defined by the amount of transactions and their total transaction volume.
Given two tables: and where has columns and has columns , write a SQL query to find out the top 10 users who have done the most transactions and the total transaction volume is highest in the past year.
Example Input:
| user_id | username | |
|---|---|---|
| 1 | JohnDoe | johndoe@example.com |
| 2 | JaneDoe | janedoe@example.com |
| 3 | AliceBob | alicebob@example.com |
| 4 | CharlieDan | charliedan@example.com |
| 5 | EveFrank | evefrank@example.com |
Example Input:
| transaction_id | user_id | transaction_amount | transaction_date |
|---|---|---|---|
| 1001 | 1 | 100.00 | 2021-08-01 00:00:00 |
| 1002 | 2 | 200.00 | 2021-08-01 00:00:00 |
| 1003 | 3 | 300.00 | 2021-08-02 00:00:00 |
| 1004 | 1 | 150.00 | 2021-08-03 00:00:00 |
| 1005 | 2 | 250.00 | 2021-08-03 00:00:00 |
Answer:
The above PostgreSQL query joins the and tables on and then filters out transactions that happened in the past year. It then groups by , , and to calculate the and . Finally, it orders the results by and and limits the output to the top 10 users.
To practice a similar customer analytics SQL question where you can code right in the browser and have your SQL code instantly graded, try this Walmart SQL Interview Question:
SQL Question 2: Employee Salaries Higher Than Their Manager
Given a table of Asseco employee salary data, write a SQL query to find all employees who earn more money than their direct manager.
Asseco 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, which is more than her manager William Davis who earns 7,800.
Code your solution to this problem 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 solution above is hard to understand, you can find a detailed solution here: Employees Earning More Than Managers.
SQL Question 3: How does a cross join differ from a natural join?
A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.
For example, say you worked on the Marketing Analytics team at Asseco, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.
For an example of each one, say you had sales data exported from Asseco's Salesforce CRM stored in a datawarehouse which had two tables: and .
An (which is a type of natural join) combines the two tables on the common
This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.
One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.
SQL Question 4: Calculate Average Monthly Rating for Products
Assume Asseco is selling a variety of software products and receives reviews from customers for each product. The goal is to analyze how each product is doing on a monthly basis. Write a SQL query that calculates the average rating for each product per month, ordered by the product id and then month.
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 |
Example Output:
| mth | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
This query uses the GROUP BY clause to separate the data into groups of unique products each month. The AVG function is then applied to each group to calculate the average rating per product per month. The output is ordered first by product_id and then by month.
To practice a related window function SQL problem on DataLemur's free online SQL code editor, try this Google SQL Interview Question:
SQL Question 5: What does the function do, and when would you use it?
The function is used to remove NULLs, and replace them with another value.
For example, say you were a Data Analyst at Asseco and were doing a customer analytics project to see who the most marketing-engaged customers were, and had access to the below table.
asseco_customers:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | very_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | mildly_active |
| 303 | NULL | NULL |
Before you could procede, you had to remove the NULLs, and replace them with the default value (not_active for email, and not_opted_in for SMS). To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | very_active | not_opted_in |
| 201 | un-subscribed | not_opted_in |
| 301 | not_active | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | mildly_active |
| 303 | not_active | not_opted_in |
SQL Question 6: Retrieve High-Paying Customers from Asseco
As a data analyst at Asseco, you are tasked with filtering customer records from the company's customer database. The company defines a "high-paying customer" as a customer with a total payment of more than $1000 during the last year. Furthermore, Asseco is also interested in customers who are either from 'Texas' or 'New York'. Retrieve a list of customers meeting the said criteria. For each qualifying customer, the output should include the Customer ID, First Name, Last Name, State, and Total Payment.
Example Input:
| customer_id | first_name | last_name | state |
|---|---|---|---|
| 001 | John | Doe | Texas |
| 002 | Jane | Smith | New York |
| 003 | Tom | Roberts | California |
| 004 | Alice | Johnson | Texas |
Example Input:
| payment_id | customer_id | payment_date | amount |
|---|---|---|---|
| 1001 | 001 | 2021-09-10 | 600 |
| 1002 | 001 | 2021-12-12 | 500 |
| 1003 | 002 | 2022-08-17 | 500 |
| 1004 | 003 | 2022-06-22 | 1000 |
| 1005 | 004 | 2021-12-30 | 1100 |
Answer:
In the provided query, a join is performed between the Customers and Payments tables on the customer_id column. The WHERE clause is used to filter out payments made in the last year, and customers from either Texas or New York. The GROUP BY clause creates groups for each customer and the HAVING clause filters these groups for customers who have paid more than $1000. Finally, the results are ordered by the total payment in descending order. This provides a list of high-paying customers from Texas or New York for the last year.
SQL Question 7: In SQL, are blank spaces the same as a NULL?
A value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values.
It's important to handle values properly in SQL because they can cause unexpected results if not treated correctly. For example, comparisons involving values always result in . Also values are not included in calculations. For example, will ignore values in the column.
SQL Question 8: Calculate Click-Through and Add-to-Cart Conversion Rates for Digital Products
Asseco is a company selling a variety of digital products. They are interested in understanding their click-through rates (CTR) from ads to product views, and how many of these views then convert to adding a product to the cart (Conversion Rate).
You are given 3 tables:
table captures every click on an ad and the user who clicked it:
table stores data when users view a product:
table, which logs every instance of a user adding a product to their cart:
Your task is to calculate the ad CTR and add-to-cart conversion rates per product. Assume that product views and add-to-cart actions are only counted if they follow a click on an ad within the same day.
Answer:
The query starts with joining the and tables to calculate the total number of ad clicks and the number of product views following those clicks per product. A second CTE () accomplishes a similar task for views to cart adds. We then join the two CTEs on the and calculate the click-through and conversion rates by product.
To practice a similar SQL problem on DataLemur's free online SQL code editor, solve this SQL interview question asked by Facebook:
SQL Question 9: Average Monthly Software Sales
Asseco is a technology and software solutions company. They have software products that they sell to their various clients monthly. There is a need to analyze how well each of their software products is performing on a monthly basis. To do this, we want to find the average sales revenue per month for each of the software products.
Here is some sample data:
Example Input:
We want to produce an output that groups the sales by month and software, providing the average sales revenue for each group:
Example Output:
Answer:
This query uses the operator to group the records by month and software products. Then it calculates the average revenue of sales for each group using the aggregate function. The function is used to get the month from the sale_date column. Finally, the clause orders the final result based on the month in ascending order, and in case of ties, it will further order by average revenue in descending order.
SQL Question 10: What does the constraint do, and when might you use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
Preparing For The Asseco SQL Interview
The key to acing a Asseco SQL interview is to practice, practice, and then practice some more!
In addition to solving the above Asseco SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.
Each problem on DataLemur has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there's an interactive coding environment so you can easily right in the browser your query and have it checked.
To prep for the Asseco SQL interview it is also helpful to practice SQL problems from other tech companies like:
However, if your SQL foundations are weak, don't worry about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers SQL concepts such as filtering data with WHERE and functions like SUM()/COUNT()/AVG() – both of these pop up frequently during Asseco interviews.
Asseco Data Science Interview Tips
What Do Asseco Data Science Interviews Cover?
Besides SQL interview questions, the other topics to prepare for the Asseco Data Science Interview are:
- AB Testing & Experimentation Questions
- Python or R Coding Questions
- Product-Sense Questions
- Machine Learning Questions
- Behavioral Based Interview Questions
How To Prepare for Asseco Data Science Interviews?
I'm a bit biased, but I think the best way to prep for Asseco Data Science interviews is to read the book Ace the Data Science Interview.
The book covers 201 interview questions sourced from Facebook, Google, & Amazon. It also has a crash course on Stats, ML, & Data Case Studies. And finally it's helped thousands of people land their dream job in data, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.