11 Kontron SQL Interview Questions (Updated 2024)

At Kontron AG, SQL used for analyzing network data for optimizing telecommunications and extracting useful insights from the IoT hardware production data for process improvement. That's why Kontron LOVES to ask SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.

So, to help you ace the Kontron SQL interview, we'll cover 11 Kontron AG SQL interview questions in this blog.

11 Kontron AG SQL Interview Questions

SQL Question 1: Finding the 'Whale Users' for Kontron

Suppose as a data analyst at Kontron, you have been tasked with identifying the 'whale users'. These are users who have made the most purchases on the Kontron platform in the last 3 months. A 'whale user' is defined as a user who has made at least 20 purchases in this period. You have access to the 'purchases' table which keeps a record of all purchases a user makes on the platform. The table looks like this below:

table

Write a SQL query to retrieve the user_id of 'whale users' and their total purchase amount.

Answer:

You can use the and clauses of SQL to solve this query.

This query groups purchases by user_id and filters only those users who have made at least 20 purchases in the last 3 months. It also calculates the total amount purchased by each 'whale user'. The results are ordered by the total purchased in descending order, meaning the 'biggest whale user' comes first.

To work on another SQL customer analytics question where you can solve it interactively and have your SQL code automatically checked, try this Walmart Labs SQL Interview Question:

SQL Question 2: Department Salaries

You're given a table of Kontron employee and department salaries. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.

Write a SQL query for this interview question interactively on DataLemur:

The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.

SQL Question 3: What are the similarities and differences between a clustered index and non-clustered index?

Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.

A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.

In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.

SQL Question 4: Calculate Monthly Star Averages for Kontron's Products

As a Data Analyst at Kontron, your task is to monitor the average ratings (stars) given by users to different products on a monthly basis. Each product has a unique product_id, and users submit reviews with a star rating (from 1 to 5, 5 being the best). Write a SQL query to calculate the average star rating for each product for each month (assume that submit_date is in the format YYYY-MM-DD).

For simplicity, you can also assume that the table has the following columns:

• : unique identifier of the review
• : unique identifier of the user who submitted the review
• : date when the review was submitted
• : unique identifier of the product which was reviewed
• : rating given by the user (in stars)

Example Input:

Example Output:

Answer:

In this query, we first extract the month from the because we're interested in monthly averages. The ratings are then averaged for each month and product. We convert stars to decimal to avoid integer division problems. We group the data by month and product to ensure we have separate averages for each product per month. The results are finally ordered by month and product for easier interpretation.

To solve another window function question on DataLemur's free interactive coding environment, solve this Amazon SQL Interview Question:

SQL Question 5: Can you explain the difference between the and window functions in SQL?

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 RANK() 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 Kontron:

To rank these salespeople, we could execute the following query:

The result of this query would be:

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. Sure, Here's a typical SQL interview question related to filtering data.

SQL Question 6: Filter Customers based on their purchase details and location.

Kontron is a global leader in embedded computing technology (ECT). As an embedded computing technology leader, you are tasked to help the sales team focus their efforts on customers who are likely to purchase a specific product, P567, and live in a specific area, say Europe. The database has two tables - that holds customer ids, names, and their location, and that holds sales records including the customer id, product id, sales date, and the total amount.

Here are some example input tables for the problem:

Example Input:

Example Input:

The question is, can you write a SQL query that returns the customer names who are from Europe and have purchased product P567?

Answer:

You can achieve this using a coupled with the clause to filter on the conditions:

This query first combines the and tables based on their . It then applies the specific filters - location must be Europe and product_id must be P567 - in the clause. The result is the list of customer names who meet these conditions.

SQL Question 7: Can you explain what / SQL commands do?

The EXCEPT operator is used to return all rows from the first SELECT statement that are not returned by the second SELECT statement. Note that is available in PostgreSQL and SQL Server, and it's equivalent operator is called and is available in MySQL and Oracle.

For a tangible example, suppose you were doing an HR Analytics project for Kontron, and had access to Kontron's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all employees who never were a contractor using this query:

SQL Question 8: Calculate average equipment downtime in hours per day for Kontron

Kontron is a company that develops embedded computing technology, which powers many industrial systems and applications. As an embedded systems analyst, your task is to calculate the average downtime in hours per day for the equipment maintained by Kontron. You are provided with a table "equipment_logs", each row of which represents a record of the equipment going offline or online.

Example Input:

Consider that the equipment goes offline and online multiple times a day, but is expected to be online by the start of the next day.

Example Output:

Answer:

This query first extracts the date from the timestamp and then uses a conditional aggregate function to calculate the difference in hours between the first 'offline' status and the last 'online' status of the equipment for each day. The AVG function is then applied to this result to get the average downtime in hours for each day.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total time of a system's operation or this Tesla Unfinished Parts Question which is similar for <tracking stages of equipment in a production process.

SQL Question 9: Analyzing Kontron's click-through-rate

Kontron, a technology company, runs multiple online marketing campaigns. They want to measure the effectiveness of their campaigns by analyzing the click-through rates. Given a table that logs all the clicks on their digital ads and another table that logs all the page-visits, calculate the click-through-rate (CTR) for each campaign_id. The CTR is defined as the number of unique users who clicked on an ad divided by the number of unique users who visited the page. Consider only the users who visited the page after they clicked the ads.

Example Input:

Example Input:

Answer:

This query calculates the number of unique users who clicked on an ad (unique_clicks) and the number of unique users who visited the page after clicking the ad (unique_visits) for each campaign. Then, it calculates the CTR by dividing unique_clicks by unique_visits. Please note that the join condition also makes sure that only the page visits occurring after the ad click are considered.

To solve a related SQL interview question on DataLemur's free interactive SQL code editor, attempt this Facebook SQL Interview question:

SQL Question 10: What's the difference between the and window function?

Both the and window functions are used to access a row at a specific offset from the current row.

However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.

Often, the offset for both functions is 1, which gives access to the immediately following/preceding row. Here's a SQL query example:

SQL Question 11: Filtering Client Records

As a database analyst for Kontron, your responsibility includes reviewing customer records and identifying specific patterns. One of your tasks involves finding all clients whose addresses contain the word 'Street'. You have been provided with the database as shown in the sample.

Example Input:

The output should list all clients with 'Street' in their address.

Example Output:

Answer:

Here is a PostgreSQL query that filters out the required result from the clients table.

This query uses the LIKE keyword in SQL to filter the clients with 'Street' in their address. The '%' sign is a wildcard character that matches any sequence of characters. Thus, '%Street%' will match any address that contains the word 'Street', regardless of what characters come before or after it.

How To Prepare for the Kontron SQL Interview

The best way to prepare for a Kontron SQL interview is to practice, practice, practice. Beyond just solving the above Kontron SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.

Each DataLemur SQL question has hints to guide you, step-by-step solutions and best of all, there is an online SQL coding environment so you can right online code up your SQL query answer and have it executed.

To prep for the Kontron SQL interview it is also a great idea to solve SQL questions from other tech companies like:

• Advantech SQL questions

• Cisco SQL questions

• IBM SQL questions

• PTC SQL questions

In case your SQL query skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers SQL topics like Self-Joins and CASE/WHEN statements – both of which pop up often in Kontron SQL interviews.

Kontron AG Data Science Interview Tips

What Do Kontron Data Science Interviews Cover?

In addition to SQL interview questions, the other topics to practice for the Kontron Data Science Interview are:

• Statistics and AB Testing Questions
• Python or R Programming Questions
• Product Analytics Questions
• ML Modelling Questions
• Behavioral & Resume-Based Questions

How To Prepare for Kontron Data Science Interviews?

I'm sorta biased, but I believe the optimal way to study for Kontron Data Science interviews is to read the book I wrote: Ace the Data Science Interview.

The book has 201 data interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google). It also has a crash course on Python, SQL & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.