# 11 Qt Group SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At Qt Group, SQL is used frequently for analyzing user interaction with applications and managing databases for software development projects. That's why Qt Group often tests SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.

As such, to help you prepare for the Qt Group SQL interview, we've collected 11 Qt Group SQL interview questions – can you solve them?

## 11 Qt Group SQL Interview Questions

### SQL Question 1: Identify Most Active Customers (Power Users) for the Qt Group

Assume that the business metric we are interested in, is their purchasing behavior with a focus on the number of purchases. The Qt Group would like to identify their power users who have the most number of purchases done in the last 12 months. We are looking for customers whose total purchases are within the top 5 percentile of users.

Given 2 tables, table containing user details and table with details of the purchases made.

Please write a SQL query to find the details of the power users who land in the top 5 percentile of users in terms of total purchases.

Provided table schemas:

user_idfirst_namelast_nameemailsignup_date
1JohnDoejohn.doe@example.com2020-01-01
2JaneSmithjane.smith@example.com2020-02-01
3EmmaJohnsonemma.johnson@example.com2020-03-01
4EmilyJonesemily.jones@example.com2019-11-01
purchase_iduser_idpurchase_dateproduct_idpurchase_amount
401512022-05-0550199.50
401622022-05-06502120.00
401712022-05-08502120.00
401832022-05-08503125.00

Assuming the current date is 2022-06-01, the Postgres SQL query would be:

This query first calculates the total purchases of users and then gets a purchase cutoff in the 95th percentile. Then it joins this data with users' details to get the needed information on power users who have total purchases that meet or surpass the cutoff.

Note: The 'PERCENTILE_CONT' function is used to calculate the 95 percentile of the total number of purchases made by each user. It is an important function for this type of analysis as it allows to identify a threshold beyond which are the 5% top users.

To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:

### SQL Question 2: Calculate Monthly Active Users

Qt Group, among other things, develops a variety of software tools used by developers. We have recently implemented a feature to track how many unique users are using our software tools each month. We need to calculate the Monthly Active Users (MAU) from the data stored in a table 'activity_log'. Create a SQL query to do the same.

Here's the structure and sample data from the 'activity_log' table:

##### Example Input:

SQL query should return the number of unique users for each month for each tool. Please ignore the year for simplicity.

This query first extracts month from activity_date using the 'DATE_PART' function. It then groups the result set by 'month' and 'tool_id'. 'COUNT(DISTINCT user_id)' function is used to count unique active users in each group. The result is then ordered by 'month' and 'tool_id'.

For more window function practice, try this Uber SQL Interview Question on DataLemur's interactive coding environment:

### SQL Question 3: When would you use the constraint?

A is like a secret code that unlocks the door to another table. It's a field in one table that points to the (the master key) in another table. This helps keep the data in your database organized and tidy, because it won't let you add new rows to the table unless they have the correct secret code (a corresponding entry in the table).

It's also like a special bond between the two tables - if you try to delete the data, the will be like "Whoa, hold on! I still need that information!" and prevent the deletion from happening.

### SQL Question 4: Filter Customers Based on Purchase History and Location

Qt Group is an established company with a global customer base. For marketing purposes, they would like a query that identifies customers in a certain city who have made a purchase above a certain value within the last year.

##### Example Input:
customer_idfirst_namelast_namecity
001JohnDoeNew York
002JaneSmithLondon
003SteveJohnsonNew York
004LisaBrownTokyo
##### Example Input:
order_idcustomer_idpurchase_datetotal_value
900100106/20/2021250.00
900200206/05/2021300.00
900300308/18/2020100.00
900400102/15/2022500.00
900500412/26/2021400.00

In this scenario, Qt Group wants to identify customers in New York who have made a purchase of over \$200 in the last year.

With this query, we first specify the two tables we want to pull data from, and . We join these tables on their common column . Then we filter the resulting data set to only row where the purchase date is within the last year, the city is "New York", and the total value of the order is over 200.

### SQL Question 5: What's the difference between relational and NoSQL databases?

While knowing this answer is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at Qt Group should be at least aware of SQL vs. NoSQL databases.

Relational databases and non-relational (NoSQL) databases have some key differences, particularly in terms of how data is stored. Whereas relational databases have tables, made up of rows and columns, NoSQL databases use various data models like:

• Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row within the same table
• Key-Value Stores – instead of rows and columns, you have keys, where each key is associated with only one value in a collection (similar to a Python dictionary data structure!)
• Graph Stores – instead of rows of data, you have nodes, and then can also have edges between entities (much like a Graph Data Structure for those who've taken a Computer Science data structures & algorithms class)

This added flexibility makes NoSQL databases well-suited for handling non-tabular data or data with a constantly changing format. However, this flexibility comes at the cost of ACID compliance, which is a set of properties (atomic, consistent, isolated, and durable) that ensure the reliability and integrity of data in a database. While most relational databases are ACID-compliant, NoSQL databases may not provide the same level of guarantees.

### SQL Question 6: Calculate the Average Usage Time of Software Products

As a SQL developer at Qt Group, they can ask you to find out the average usage time of various software products during the last month. This information can be used to understand better which products are seeing heavy usage and may need more resources or possible improvements/updates.

##### Example Input:
usage_iduser_idsoftware_idusage_dateusage_hours
101789500210/01/20225
202654500210/02/20227
303123600210/03/20224
404678600210/04/20226
505345600210/05/20228
##### Example Input:
software_idsoftware_name
5002Qt Creator
6002Qt Designer
##### Example Output:
softwareavg_usage_hours
Qt Creator6.00
Qt Designer6.00

Your PostgreSQL query to solve the problem would look something like this:

In this query, we join the software_usage table with the software_products table on the software_id. We filter for the records from the last month and then calculate the average usage time for each software product.

This result gives an overview of the average usage time of each software product during the previous month. It helps the company to track the usability of the software and plan resources or updates accordingly. It's a useful metric for any software company to understand the usage trend of their applications.

### SQL Question 7: In the context of database schema design, can you give an example of a one-to-one relationship vs. a one-to-many relationship?

In database schema design, a one-to-one relationship is when each entity is associated with only one instance of the other. For instance, a US citizen's relationship with their social-security number (SSN) is one-to-one because each citizen can only have one SSN, and each SSN belongs to one person.

A one-to-many relationship, on the other hand, is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a person and their email addresses - one person can have multiple email addresses, but each email address only belongs to one person.

### SQL Question 8: Analyze Click-Through and Conversion Rates

Given two tables, and , from Qt Group's database where contains the logs of users clicking to view a product and the contains logged actions of users adding a product to their cart. Determine the clickthrough conversion rates of users from clicking on a product to adding it to their cart on a daily basis.

##### Example output (rate is expressed as percentage):

This query first calculates the daily number of clicks and add to cart actions for each product, and then calculates the click-through conversion rate as the ratio of to . If there are any values after the join, implying there were no add to cart actions for a specific day and product combo, we replace them with 0. Finally, we order the results by date and then by product_id for ease of interpretation.

To practice another question about calculating rates, solve this TikTok SQL Interview Question on DataLemur's interactive SQL code editor:

### SQL Question 9: Find Customers belong to Specific Country

Imagine you're working for the Qt Group, and the company needs a list of all customers who are from United States. The Qt Group uses a database that has a table called "customers" with fields including , , , , and .

Your task is to write an SQL query that makes use of the SQL keyword LIKE to filter all records of customers from the United States.

##### Example Input:
customer_idfull_nameemailcountryjoin_date
1John Doejohndoe@gmail.comUnited States2020/01/01
3Michael Johnsonmichael.j1990@yahoo.comUnited States2021/02/17
4Olivia Brownobrown@outlook.comUnited Kingdom2021/07/19
5William Taylorwill.taylor@gmail.comUnited States2019/09/12

This query uses the LIKE operator in the WHERE clause to filter all the customers who are from the United States. Once executed, the query returns all columns from the table for those records where the field matches the string 'United States'.

### SQL Question 10: What do stored procedures do, and when would you use one?

Stored procedures are like functions in Python – they can accept input params and return values, and are used to encapsulate complex logic.

For example, if you worked as a Data Analyst in support of the Marketing Analytics team at Qt Group, a common task might be to find the conversion rate for your ads given a specific time-frame. Instead of having to write this query over-and-over again, you could write a stored procedure like the following:

To call this stored procedure, you'd execute the following query:

### SQL Question 11: Analyze Customer Orders via a SQL Join Query

For Qt Group, we maintain a customer database where we record customer details and their orders. We have the table tracking each customer's details, and the table tracking each order made by these customers.

The table has columns - customer_id, first_name, last_name, email, signup_date, city and country.

The table has columns - order_id, product_id, customer_id, order_date, and quantity.

Write a SQL query to obtain the total quantity of each product ordered by customers from 'Los Angeles' city.

##### Example Input:
customer_idfirst_namelast_nameemailsignup_datecitycountry
111JohnDoejohn_doe@example.com2020-02-01Los AngelesUSA
222JaneSmithjane_smith@example.com2019-06-03Los AngelesUSA
333SteveBrownsteve_brown@example.com2021-01-11New YorkUSA
##### Example Input:
order_idproduct_idcustomer_idorder_datequantity
50011011112022-02-0110
50021022222022-02-1515
50031011112022-03-015
50041033332022-03-1020
50051022222022-04-0110

This query first performs an inner JOIN operation between and tables using as the common key. It then filters out the rows where the customer city is 'Los Angeles'. After this, the query groups the data by and sums up the for each , thus providing the total quantity of each product ordered by customers from Los Angeles.

Since joins come up frequently during SQL interviews, take a stab at an interactive Spotify JOIN SQL question:

### Qt Group SQL Interview Tips

The best way to prepare for a Qt Group SQL interview is to practice, practice, practice. Besides solving the above Qt Group SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each exercise has hints to guide you, detailed solutions and crucially, there is an interactive coding environment so you can right in the browser run your SQL query answer and have it checked.

To prep for the Qt Group SQL interview it is also a great idea to practice SQL problems from other tech companies like:

In case your SQL query skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this interactive SQL tutorial.

This tutorial covers things like CASE/WHEN statements and creating pairs via SELF-JOINs – both of these come up routinely during SQL job interviews at Qt Group.

### Qt Group Data Science Interview Tips

#### What Do Qt Group Data Science Interviews Cover?

In addition to SQL query questions, the other topics to practice for the Qt Group Data Science Interview are:

#### How To Prepare for Qt Group Data Science Interviews?

To prepare for Qt Group Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
• a refresher on SQL, Product-Sense & ML
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