8 Viatris SQL Interview Questions (Updated 2024)
Viatris employees write SQL queries for analyzing pharmaceutical data trends, helping them track the effectiveness of various medications and identify areas for improvement in patient care. They also manage patients' drugs and dosage information, ensuring that healthcare providers have accurate data to make informed decisions, this is the reason why Viatris asks SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you prep, here’s 8 Viatris SQL interview questions – able to answer them all?
8 Viatris SQL Interview Questions
SQL Question 1: Calculate the average monthly ratings for each product
Viatris is a leading global healthcare company and for their products, they have a rating system where users can rate their products. You're given a table with product ratings submitted by users for different products over a period of time. Each product is identified by its id. Users rate products and provide a for the reference.
Your task is to write a SQL query to calculate the average rating of each product on a monthly basis. The month can be extracted from the field, which is of type date.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-01-05 | 50001 | 4 |
| 7802 | 265 | 2022-01-15 | 69852 | 4 |
| 5293 | 362 | 2022-01-30 | 50001 | 3 |
| 6352 | 192 | 2022-02-06 | 69852 | 3 |
| 4517 | 981 | 2022-02-15 | 69852 | 2 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 1 | 50001 | 3.5 |
| 1 | 69852 | 4.0 |
| 2 | 69852 | 2.5 |
Answer:
This query works by first extracting the month part from the using the function. It then groups the data by the month and and calculates the average of the for each group using the function. The clause ensures that the result set is sorted first by month and then by .
For more window function practice, solve this Uber SQL problem on DataLemur's online SQL coding environment:
Explore Viatris' innovative approach to disease detection using AI, showcasing how technology is revolutionizing healthcare! Understanding these advancements can help you appreciate the role of digital therapeutics in enhancing patient outcomes.
SQL Question 2: Department Salaries
You're given a table of Viatris employee and department salary information. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Solve this problem and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department vs. Company Salary.
SQL Question 3: How is a foreign key different from a primary key in a database?
To explain the difference between a primary key and foreign key, let's start with an example Viatris sales database:
:
| order_id | product_id | customer_id | quantity |
|---|---|---|---|
| 1 | 303 | 1 | 2 |
| 2 | 404 | 1 | 1 |
| 3 | 505 | 2 | 3 |
| 4 | 303 | 3 | 1 |
In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.
and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.
SQL Question 4: Filter Customer Data
Given the table that contains information about customers and their preferences, your task is to write an SQL query that will return the information of all customers who live in the United States (), prefer the language English () and have bought medicines for diabetes ().
In reality, Viatris, a healthcare company, might need to filter out specific customer information to design target marketing strategies, measure product impact, or track sales performance. In this scenario, understanding how to write such filtering queries is critical in handling customer-related data.
Example Input:
| customer_id | name | country | language | disease |
|---|---|---|---|---|
| 1 | John | US | EN | Diabetes |
| 2 | Anna | CA | EN | Heart Disease |
| 3 | Samuel | US | ES | Diabetes |
| 4 | Emma | GB | EN | Diabetes |
| 5 | Michael | US | EN | Obesity |
| 6 | Linda | US | EN | Diabetes |
Example Output:
| customer_id | name | country | language | disease |
|---|---|---|---|---|
| 1 | John | US | EN | Diabetes |
| 6 | Linda | US | EN | Diabetes |
Answer:
In this query, we use the clause to filter out the customers based on three conditions: they live in the US, they prefer English, and they have Diabetes. The operator is used to ensure all conditions must be true for a record to be selected.
SQL Question 5: In what circumstances might you choose to denormalize a database?
There are several advantages to normalizing a database, including less redundancy, more flexibility, and better performance.
-
Less Redundancy: Normalization reduces redundancy by breaking down a larger, more general table into smaller, more specific tables. This reduces the amount of data that needs to be accessed for queries.
-
More Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (a very real reality at Viatris!)
-
Better Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.
SQL Question 6: Average Monthly Sales Volume Per Drug
As a data scientist at Viatris, a global healthcare company, one of your tasks is to analyze the sales volume of drugs the company manufactures. Specifically, you are asked to write a SQL query to find the average monthly sales volume per drug for the year 2022.
Example Input:
| sale_id | drug_id | sale_date | volume |
|---|---|---|---|
| 101 | 1001 | 01/02/2022 | 300 |
| 102 | 1002 | 01/18/2022 | 150 |
| 103 | 1001 | 02/10/2022 | 200 |
| 104 | 1002 | 02/15/2022 | 250 |
| 105 | 1002 | 02/22/2022 | 200 |
| 106 | 1001 | 03/05/2022 | 350 |
| 107 | 1002 | 03/20/2022 | 300 |
Example Output:
| mth | drug | avg_sales_volume |
|---|---|---|
| 1 | 1001 | 300.00 |
| 1 | 1002 | 150.00 |
| 2 | 1001 | 200.00 |
| 2 | 1002 | 225.00 |
| 3 | 1001 | 350.00 |
| 3 | 1002 | 300.00 |
Answer:
This code extracts the month from the and uses it along with to group the sales. The function calculates the average sales volume per month for each drug. The clause filters the sales made in 2022. Finally, the results are ordered by month and .
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating year-on-year sales data or this Amazon Average Review Ratings Question which is similar for aggregating data per month.
SQL Question 7: What is the difference between the and window functions?
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 Viatris:
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.
SQL Question 8: Calculating the Click-Through-Rate for Viatris Ads
Viatris is a company that markets various pharmaceutical products and they have a website from which customers can learn about their products and make purchases. As part of their online marketing strategy, they regularly run digital ads on selected platforms. For each ad, they measure both the number of times the ad was seen (impressions) and how many times it was clicked on (clicks).
The Click-Through-Rate (CTR), a key performance indicator for digital advertisement, is defined as the ratio of users who click on an ad to the number of total users who viewed the ad (impressions).
Given a table representing impressions generated by Viatris ads and a table for clicks on those ads, your task is to compute the CTR for each ad.
Example Input:
| impression_id | ad_id | time_stamp | user_id |
|---|---|---|---|
| 101 | 1 | 07/15/2022 00:00:00 | 501 |
| 102 | 2 | 07/15/2022 00:10:00 | 610 |
| 103 | 1 | 07/15/2022 00:20:00 | 620 |
| 104 | 1 | 07/15/2022 01:30:00 | 650 |
Example Input:
| click_id | ad_id | time_stamp | user_id |
|---|---|---|---|
| 501 | 1 | 07/15/2022 00:01:00 | 501 |
| 502 | 1 | 07/15/2022 01:31:00 | 650 |
Answer:
This query first aggregates impressions and clicks for each ad. This is done inside the subqueries and . These derived tables are then joined together on the column to compute the CTR. The CTR is computed as the number of clicks divided by the number of impressions. Note that we are using a left join here to include ads that had impressions but no clicks, which would give them a CTR of 0.
To practice a related SQL problem on DataLemur's free interactive coding environment, try this Meta SQL interview question:
How To Prepare for the Viatris SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Viatris SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Viatris SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each interview question has multiple hints, detailed solutions and crucially, there is an interactive coding environment so you can instantly run your query and have it graded.
To prep for the Viatris SQL interview it is also a great idea to practice SQL problems from other healthcare and pharmaceutical companies like:
- Moderna SQL questions
- Tenet Healthcare SQL questions
- Becton Dickinson SQL questions
- Amgen SQL questions
In case your SQL query skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.
This tutorial covers things like LEAD/LAG and Self-Joins – both of which come up routinely during Viatris SQL assessments.
Viatris Data Science Interview Tips
What Do Viatris Data Science Interviews Cover?
Besides SQL interview questions, the other types of questions covered in the Viatris Data Science Interview are:
- Probability & Statistics Questions
- Coding Questions in Python or R
- Product-Sense Questions
- Machine Learning Questions
- Behavioral Interview Questions focussed on Viatris culture and values
How To Prepare for Viatris Data Science Interviews?
To prepare for Viatris Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG (FB, Apple, Amazon, Netflix, Google)
- a crash course on SQL, AB Testing & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't forget about the behavioral interview – prepare for it using this Behavioral Interview Guide for Data Scientists.