At Software AG, SQL is crucial for analyzing and manipulating large data sets for predictive analysis, especially in their process mining division. So, it shouldn't surprise you that Software AG asks SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
Thus, to help you study for the Software AG SQL interview, here’s 8 Software AG SQL interview questions – how many can you solve?
As a data analyst at Software AG, you have been given a task to analyze the product reviews. You need to develop a SQL query to calculate average product rating each month. The goal is to track how the average rating of each product changes month by month over time.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
101 | 123 | 06/01/2022 | 101 | 4 |
102 | 456 | 06/10/2022 | 102 | 5 |
103 | 789 | 06/15/2022 | 101 | 2 |
104 | 123 | 07/05/2022 | 101 | 5 |
105 | 456 | 07/10/2022 | 102 | 4 |
106 | 789 | 07/15/2022 | 101 | 3 |
month | product_id | avg_stars |
---|---|---|
6 | 101 | 3 |
6 | 102 | 5 |
7 | 101 | 4 |
7 | 102 | 4 |
This SQL query extracts the month from the submit_date column, groups the data by month and product_id, and then calculates the average stars for each group. The results are ordered by month and product_id for easy parsing.
By using the GROUP BY with the extracted month and product_id, we ensure that the average is correctly calculated for each product each month. The AVG() function is a built in function in PostgreSQL which gives us the average of a numerical column.
The ORDER BY clause orders the results first by month, then by product_id. As the dataset grows, this ordering ensures that the results are easy to parse and identify trends or changes month by month for each product.
Please note that , and are all hypothetical columns in the table. Be sure to replace these with the correct column names as represented in your specific database schema.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
As a data analyst at Software AG, your task is to identify the customers who are from the 'software' sector and have purchased a specific product at least thrice in the last year. You need to filter out customer records that meet these conditions.
The 'customers' and 'orders' tables are structured as follows:
customer_id | name | sector |
---|---|---|
1 | ABC Corp | software |
2 | XYZ Ltd | hardware |
3 | Software AG | software |
order_id | customer_id | product | order_date |
---|---|---|---|
1001 | 1 | Product-A | 01/15/2022 |
1002 | 1 | Product-A | 03/07/2022 |
1003 | 1 | Product-A | 08/29/2022 |
1004 | 2 | Product-A | 04/16/2022 |
1005 | 1 | Product-B | 06/11/2022 |
1006 | 3 | Product-A | 09/20/2022 |
You need to query these two tables and filter out the customer records meeting the above conditions.
This query first generates a subquery that groups orders by customer_id, filters by 'Product-A', and counts the number of products each customer purchased in the year 2022. It then joins this subquery with the 'customers' table by 'customer_id', and filters by 'software' sector and purchase_count greater than or equal to 3, thereby giving us customers from the software sector who have purchased 'Product-A' at least three times in the year 2022.
The operator merges the output of two or more statements into a single result set. The two SELECT statements within the UNION must have the same number of columns and the data types of the columns are all compatible.
For example, if you were a Data Analyst on the marketing analytics team at Software AG, this statement would return a combined result set of both Software AG's Google and Facebook ads that have more than 300 impressions:
As a data analyst at Software AG, you are tasked with calculating the average number of defects in software applications per category. For the purpose of this question, assume we have two tables: {#Question-4}
The table, which includes , , and :
software_id | software_name | category |
---|---|---|
1 | Alpha | Productivity |
2 | Beta | Analytics |
3 | Gamma | Productivity |
4 | Delta | Analytics |
5 | Epsilon | Communication |
The table, which includes , (foreign key referencing ), and .
defect_id | software_id | defect_severity |
---|---|---|
101 | 1 | 3 |
102 | 2 | 4 |
103 | 1 | 2 |
104 | 3 | 5 |
105 | 2 | 3 |
106 | 4 | 4 |
107 | 5 | 1 |
You are required to find the average for each and to format the result as follows:
category | avg_defect_severity |
---|---|
Productivity | 3.33 |
Analytics | 3.67 |
Communication | 1.00 |
The following SQL query can be used to solve the problem statement:
This query joins the and tables on , groups the result by the from the table, and then calculates the average for each group. Finally, it selects the and as per the question's requirements.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing categories in a dataset or this Amazon Average Review Ratings Question which is similar for calculating averages.
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Software AG employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships 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.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that salary values are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the employee hire date to the current date if no value is provided when a new employee is added to the database.
As a hiring manager in Software AG, you need to find candidates from the company database who have "Software Engineer" in their job title.
Specifically, you want to see their profile id, first name, last name, job title and email address.
The profile data of candidates are stored in the database table with columns , , , and .
profile_id | first_name | last_name | job_title | |
---|---|---|---|---|
1001 | John | Doe | Software Engineer | johndoe@example.com |
1002 | Jane | Smith | Senior Software Engineer | janesmith@example.com |
1003 | Sam | Robinson | Data Scientist | samrobinson@example.com |
1004 | Alice | Johnson | Software Engineer Intern | alicejohnson@example.com |
1005 | Bob | Davis | Database Administrator | bobdavis@example.com |
This query uses the LIKE keyword combined with the '%Software Engineer%' pattern to filter down to only the rows where the job_title includes 'Software Engineer'. The columns specified in the SELECT clause (profile_id, first_name, last_name, job_title, email) will then be returned for these rows.
profile_id | first_name | last_name | job_title | |
---|---|---|---|---|
1001 | John | Doe | Software Engineer | johndoe@example.com |
1002 | Jane | Smith | Senior Software Engineer | janesmith@example.com |
1004 | Alice | Johnson | Software Engineer Intern | alicejohnson@example.com |
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
For example, if you have a table of Software AG customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Software AG customers table.
The constraint helps maintain the integrity of the data in the database by preventing the insertion of rows in the table that do not have corresponding entries in the table. It also enforces the relationship between the two tables and prevents data from being deleted from the table if it is still being referenced in the table.
You have a database that stores product information, their production cost and the selling price. Your task is to create a query that calculates the profit margin for each product. Profit margin is calculated by subtracting the production cost from the selling price and dividing by the selling price, then multiplying by 100 to get the profit margin as a percentage.
We will also use the ROUND() function to round the result to 2 decimal places. Furthermore, identify the product(s) where the profit margin is more than 30%.
product_id | product_name | production_cost | selling_price |
---|---|---|---|
001 | Product A | 80 | 200 |
002 | Product B | 150 | 300 |
003 | Product C | 110 | 250 |
004 | Product D | 50 | 100 |
005 | Product E | 90 | 120 |
product_id | product_name | profit_margin |
---|---|---|
001 | Product A | 60.00 |
002 | Product B | 50.00 |
003 | Product C | 56.00 |
In the SQL block, we're calculating the difference between the selling price and the production cost for each product to determine profit. This is divided by the selling price to get a ratio, which is then multiplied by 100 to get a percentage representing the profit margin. The final output is rounded to 2 decimal places with the ROUND() function for more convenient reading.
The WHERE clause is then used to filter out any products where the profit margin is not greater than 30%. Thus, we are only left with products that give more than 30% profit margin.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating metrics for products or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for doing profitability calculations.
The best way to prepare for a Software AG SQL interview is to practice, practice, practice. Besides solving the above Software AG SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Facebook, Google and unicorn tech startups.
Each problem on DataLemur has hints to guide you, step-by-step solutions and most importantly, there's an interactive SQL code editor so you can easily right in the browser your query and have it checked.
To prep for the Software AG SQL interview you can also be useful to practice SQL problems from other tech companies like:
However, if your SQL skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers SQL topics like joining a table to itself and SQL joins with practice exercises – both of these pop up frequently during Software AG SQL interviews.
In addition to SQL query questions, the other types of problems tested in the Software AG Data Science Interview are:
To prepare for Software AG Data Science interviews read the book Ace the Data Science Interview because it's got: