At Dentsply Sirona, SQL is essential for analyzing dental product usage patterns, helping the company understand how their products are utilized in various dental practices. They also use SQL to optimize supply chain efficiency by querying and manipulating their vast and complex databases, ensuring they have the right supplies available when needed, this is why Dentsply Sirona asks SQL questions in interviews for Data Science, Analytics, and Data Engineering jobs.
Thus, to help you practice, we've curated 9 Dentsply Sirona SQL interview questions – can you solve them?
Dentsply Sirona is a company that primarily deals with dental supplies and equipment. They would be interested in identifying their "power users". In this context, power users are dentists or dental clinics that frequently purchase their products. Specifically, let's say a power user is defined as one that purchases an average of at least 300 units per month from any product category in the last 6 months.
Your task is to write a SQL query that returns the and of those customers who are considered power users.
purchase_id | customer_id | purchase_date | product_id | units_purchased |
---|---|---|---|---|
101 | 1001 | 01/06/2021 | 2001 | 250 |
102 | 1002 | 02/06/2021 | 2002 | 350 |
103 | 1001 | 02/06/2021 | 2002 | 400 |
104 | 1003 | 02/06/2021 | 2001 | 100 |
105 | 1003 | 03/07/2021 | 2003 | 150 |
106 | 1001 | 05/07/2021 | 2002 | 500 |
Please ensure that PostgreSQL compatibility while writing this query:
The inner query first finds the total by each for each month in the last 6 months. Then, in the outer query, it calculates the average monthly purchase for each customer and filters for those customers where this average is 300 or more.
To work on another SQL customer analytics question where you can solve it right in the browser and have your SQL code automatically checked, try this Walmart Labs SQL Interview Question:
Given a table of Dentsply Sirona employee salary data, write a SQL query to find the 2nd highest salary at the company.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Solve this interview question interactively on DataLemur:
You can find a step-by-step solution here: 2nd Highest Salary.
A DBMS (database management system), in order to ensure transactions are relaible and correct, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability
Here is what each of the ACID properties stands for:
Atomicity: ensures that a transaction is either completed in its entirety, or not completed at all. If a transaction fails halfway, the database does a rollback on the commit.
Consistency: ensures that a transaction will only be completed if adheres to the constraints defined in the database of the DB.
Isolation: ensures that concurrent transactions are isolated from each one another, so that the changes made by one transaction cannot be seen by a 2nd transaction until the 1st transaction is done.
Durability: ensures that once a transaction has been committed, the database permanently stores the results in the DB.
As you can see, it's pretty important for Dentsply Sirona's data systems to be ACID compliant, else they'll be a big problem for their customers!
As an analyst at Dentsply Sirona, a global provider of dental products and technologies, you are tasked with understanding the performance of different products in different regions. Your dataset contains the sales data of multiple products across different regions. You need to write a SQL query to find out the top selling products in each region based on the sales quantity.
Suppose that you have access to a table having the following schema:
sale_id | product_id | region | sell_date | quantity_sold |
---|---|---|---|---|
2211 | 891 | North | 06/16/2023 00:00:00 | 25 |
3382 | 122 | South | 07/06/2023 00:00:00 | 42 |
4291 | 345 | West | 07/15/2023 00:00:00 | 35 |
1672 | 122 | East | 08/03/2023 00:00:00 | 47 |
3017 | 345 | North | 09/23/2023 00:00:00 | 50 |
Here is the PostgreSQL query:
The inner query computes the total quantity sold for each product in each region using window function .
The outer query applies the window function to rank the products in each region based on the total quantity sold. Products with the highest are given a rank of 1.
Finally, the clause in the outer query retains products that are the top selling (rank = 1) in each region.
If run on the example input, this query result would be the top selling product in a specific region and its total quantity sold.
This analysis can help the company to focus on which products are performing best in which specific regions.
To practice a similar window function interview problem which uses RANK() on DataLemur's free online SQL coding environment, solve this Amazon BI Engineer interview question:
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.
Dentsply Sirona, a leading company in the Dental equipment industry, is designing a system to manage their inventory. They have a variety of products including dental implants, orthodontic appliances, and many others. For each product, the company wants to keep track of product ID, product name, category, supplier name, quantity on hand, and re-order level. For the suppliers, the company maintains a table to keep store supplier id, name, contact name, address and phone number. They also have a purchases table that records each purchase made with the product id, supplier id, purchase date, and quantity.
Design the database tables, their relationships, and determine which columns should go to which tables. In addition, write a SQL query that gives the list of product names that have quantities below their re-order level.
product_id | product_name | category | supplier_id | quantity_on_hand | re_order_level |
---|---|---|---|---|---|
4765 | "Oral-B Toothbrush" | Oral Care | 237 | 50 | 75 |
5689 | "Crest Whitening Toothpaste" | Oral Care | 942 | 500 | 750 |
6824 | "Invisalign Aligner" | Orthodontics | 354 | 20 | 30 |
7529 | "Dental Implant" | Implants | 158 | 10 | 15 |
supplier_id | name | contact_name | address | phone_number |
---|---|---|---|---|
237 | "P&G" | "John Doe" | "555 Sample St, Ohio" | "(555) 555-5555" |
942 | "Colgate-Palmolive" | "Jane Doe" | "123 Example Ave, New York" | "(555) 555-5555" |
354 | "Align Technology" | "Bob Smith" | "789 Markup Blvd, California" | "(555) 555-5555" |
158 | "Nobel Biocare" | "Alice Johnson" | "456 Placeholder Rd, Washington" | "(555) 555-5555" |
Here is a PostgreSQL query that lists the product names with quantities below their re-order level.
This query gives a list of product names which are below their re-order level. It uses the less than comparison operator '<' in the clause to filter out products with quantities above their re-order level.
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 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 Dentsply Sirona:
To rank these salespeople, we could execute the following query:
The result of this query would be:
name | deals_closed | rank | dense_rank |
---|---|---|---|
Akash | 50 | 1 | 1 |
Brittany | 50 | 2 | 1 |
Carlos | 40 | 3 | 2 |
Dave | 40 | 4 | 3 |
Eve | 30 | 5 | 3 |
Farhad | 10 | 6 | 4 |
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.
Imagine you are working as a Data Analyst at Dentsply Sirona, a leading provider of professional dental products and technologies. Your marketing team has executed several campaigns on the company's digital ads and have logged user interactions. You are tasked to calculate the click-through rate (CTR) of these ads.
To achieve this, you'll need to divide the number of unique users who clicked on an ad by the number of unique users who viewed the ad, then multiply the result by 100 to convert it to a percentage.
The and tables track these events on a per-ad and per-user basis. The table contains all of the ads that were seen by users. Likewise, the table records all the ads that were clicked.
id | ad_id | user_id | view_date |
---|---|---|---|
1 | 1234 | 501 | 06/08/2022 |
2 | 5678 | 501 | 06/09/2022 |
3 | 1234 | 502 | 06/10/2022 |
4 | 5678 | 503 | 06/12/2022 |
5 | 1234 | 504 | 06/12/2022 |
id | ad_id | user_id | click_date |
---|---|---|---|
1 | 1234 | 501 | 06/08/2022 |
2 | 5678 | 501 | 06/10/2022 |
3 | 1234 | 502 | 06/10/2022 |
4 | 5678 | 504 | 06/12/2022 |
Here's a possible PostgreSQL query to calculate the CTR:
This query first creates two table expressions (or CTEs): and where it counts the number of unique users who viewed and clicked on each ad respectively. Then it calculates the click-through rate using the formula specified above.
To practice a related SQL interview question on DataLemur's free online SQL coding environment, solve this Meta SQL interview question:
A common type of data that companies like Dentsply Sirona may want to filter and categorize their customers on is their email domain. This can give an idea of what type of customer they might be (e.g. University, Private Company, Government etc.).
Suppose you've been asked to filter all Dentsply Sirona's customers whose email domain is 'gmail.com'. You're given a table where each row represents a customer, their details, and their email id.
customer_id | first_name | last_name | country | |
---|---|---|---|---|
1 | John | Doe | john.doe@gmail.com | USA |
2 | Anna | Smith | anna.smith@yahoo.com | Canada |
3 | James | Johnson | james.johnson@outlook.com | UK |
4 | Emma | Williams | emma.williams@gmail.com | USA |
5 | Michael | Brown | michael.brown@dentsplysirona.com | Germany |
Write a PostgreSQL query that returns each customer with an email domain of 'gmail.com'.
This query uses the clause in SQL to filter out all customers whose email ends with '@gmail.com'. The symbol is used as a wildcard operator to match any sequence of characters.
customer_id | first_name | last_name | country | |
---|---|---|---|---|
1 | John | Doe | john.doe@gmail.com | USA |
4 | Emma | Williams | emma.williams@gmail.com | USA |
The key to acing a Dentsply Sirona SQL interview is to practice, practice, and then practice some more! Besides solving the above Dentsply Sirona SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each interview question has hints to guide you, detailed solutions and best of all, there is an interactive coding environment so you can right in the browser run your query and have it executed.
To prep for the Dentsply Sirona SQL interview you can also be wise to solve interview questions from other healthcare and pharmaceutical companies like:
However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – go learn SQL with this interactive SQL tutorial.
This tutorial covers SQL topics like LEAD/LAG window functions and CASE/WHEN statements – both of which come up often during Dentsply Sirona interviews.
In addition to SQL interview questions, the other topics to prepare for the Dentsply Sirona Data Science Interview are:
I think the optimal way to prep for Dentsply Sirona Data Science interviews is to read the book Ace the Data Science Interview.
It covers 201 data interview questions sourced from tech companies like Netflix, Google, & Airbnb. The book's also got a refresher on Python, SQL & ML. And finally it's helped a TON of people, which is why it's got over 1000+ 5-star reviews on Amazon.
While the book is more technical, it's also key to prepare for the Dentsply Sirona behavioral interview. A good place to start is by reading the company's cultural values.