11 AmerisourceBergen SQL Interview Questions (Updated 2024)
At AmerisourceBergen, SQL is essential for analyzing pharmaceutical sales data, helping the company understand market trends and customer preferences. They also use SQL to manage customer information databases, ensuring that they have accurate and up-to-date records to enhance their service, which is why AmerisourceBergen includes SQL coding questions during interviews for Data Science, Data Engineering, and Data Analytics roles.
To help you ace the AmerisourceBergen SQL interview, here's 11 AmerisourceBergen SQL interview questions in this blog.
11 AmerisourceBergen SQL Interview Questions
SQL Question 1: Identify the Power Buyers at AmerisourceBergen
AmerisourceBergen is a pharmaceutical services company that distributes medicinal products to health care providers. Let's say a crucial part of AmerisourceBergen's business comes from their regular wholesale buyers who buy medicinal products frequently in large quantities. Your task is to analyze AmerisourceBergen's database and write a PostgreSQL query to identify these thriving 'Power Buyers.' Consider someone a 'Power Buyer' if they've made purchases of at least a total quantity of 2000 units in any given month of the year.
The database has a table with columns:
- : (Primary key) The unique identifier of the order.
- : Identifier of the buyer.
- : The date when the order was placed.
- : Identifier of the ordered product.
- : The quantity of the product ordered.
Example Input:
| order_id | buyer_id | order_date | product_id | quantity |
|---|---|---|---|---|
| 10239 | 576 | 02/20/2022 | 8765 | 800 |
| 10456 | 576 | 02/21/2022 | 6543 | 650 |
| 10245 | 576 | 02/22/2022 | 8765 | 600 |
| 10873 | 890 | 03/14/2022 | 7531 | 1000 |
| 11098 | 890 | 03/20/2022 | 8765 | 1500 |
We want an output that includes the distinct months and years, the buyer_id, and the total quantity of products purchased within that month by each 'Power Buyer.'
Answer:
The above PostgreSQL query groups the orders by month, year, and buyer_id and calculates the total quantity of products purchased by each buyer in each month. The HAVING clause then filters out the results to only show the buyers who have bought at least a total of 2000 units in any month. These are the 'Power Buyers' at AmerisourceBergen.
This query enables AmerisourceBergen to identify their Power Buyers per month and year and understand their buying behaviors, which could potentially be used to develop targeted marketing strategies or to optimize inventory management.
To work on a similar customer analytics SQL question where you can code right in the browser and have your SQL solution automatically checked, try this Walmart SQL Interview Question:
Explore AmerisourceBergen's analytics solutions to see how they are leveraging data to enhance decision-making in the healthcare industry! Gaining insights into their approach can deepen your understanding of the importance of analytics in driving innovation.
SQL Question 2: Employees Earning More Than Managers
Suppose there was a table of AmerisourceBergen employee salaries. Write a SQL query to find all employees who earn more than their direct manager.
AmerisourceBergen Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Test your SQL query for this interview question directly within the browser on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the solution above is confusing, you can find a detailed solution here: Employee Salaries Higher Than Their Manager.
SQL Question 3: What's the purpose of the constraint?
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 AmerisourceBergen and an table, the column in the table could be a that references the id column (which is the primary key) in the AmerisourceBergen 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.
SQL Question 4: Calculate Monthly Average Sales per Drug
AmerisourceBergen wants to analyze the sales performance of their various drugs. They want you to write a SQL query that calculates the monthly average sale per drug considering all transactions occur in their network. The dataset provided has the following fields:
- (integer): A unique identifier for the transaction.
- (date): The date when the sale occurred.
- (integer): The unique identifier for the drug.
- (integer): The quantity of the drug sold in the transaction.
Example Input:
| transaction_id | date_of_sale | drug_id | quantity_sold |
|---|---|---|---|
| 2102 | 2021-02-15 | 110 | 620 |
| 2345 | 2021-02-20 | 111 | 530 |
| 1099 | 2021-01-26 | 110 | 660 |
| 4682 | 2021-01-15 | 112 | 200 |
| 3872 | 2021-02-10 | 112 | 430 |
The output of the query should reveal the average quantity sold per per month.
Example Output:
| month | drug_id | avg_quantity_sold |
|---|---|---|
| 2021-01-01 | 110 | 660.00 |
| 2021-01-01 | 112 | 200.00 |
| 2021-02-01 | 110 | 620.00 |
| 2021-02-01 | 111 | 530.00 |
| 2021-02-01 | 112 | 430.00 |
Answer:
This PostgreSQL query first truncates the by month using the function so it groups the sales by the start of each month. Then it groups by , calculating the average quantity of the drug sold with the function. The results are ordered by , and then .
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: What does adding to a SQL query do?
The keyword removes duplicates from a query.
Suppose you had a table of AmerisourceBergen customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.
table:
| name | city |
|---|---|
| Akash | SF |
| Brittany | NYC |
| Carlos | NYC |
| Diego | Seattle |
| Eva | SF |
| Faye | Seattle |
You could write a query like this to filter out the repeated cities:
Your result would be:
| city |
|---|
| SF |
| NYC |
| Seattle |
SQL Question 6: Analyzing Inventory Management
AmerisourceBergen is a Drug Wholesale company with distribution centers around the country. Each distribution center stores a variety of pharmaceutical products. For inventory management, the company needs to track the quantity of each product in every location in real-time.
Suppose the company has two tables: (which contains the current stock levels for each product at each location) and (which contains information about each product).
Example Input:
| inventory_id | location_id | product_id | quantity |
|---|---|---|---|
| 1 | 001 | 100 | 50 |
| 2 | 001 | 101 | 100 |
| 3 | 002 | 100 | 75 |
| 4 | 002 | 101 | 200 |
| 5 | 002 | 102 | 150 |
Example Input:
| product_id | product_name | product_price |
|---|---|---|
| 100 | Medicine A | $10.00 |
| 101 | Medicine B | $20.00 |
| 102 | Medicine C | $30.00 |
Write an SQL query to find the total inventory value for each location. The inventory value is calculated as the sum of the product's quantity in the inventory multiplied by its price.
Answer:
Using PostgreSQL, you would write the following query:
This joins the two tables on the column they share in common (). It then calculates the value of inventory at each location by multiplying the quantity of each product by its price and summing the result. The clause ensures that this calculation is done for each location separately.
SQL Question 7: How do foreign and primary keys differ?
To better understand the difference between a primary key and a foreign key, let's use an example from AmerisourceBergen's marketing analytics database, which holds data on Google Ads campaigns:
:
| ad_id | campaign_id | keyword | click_count |
|---|---|---|---|
| 1 | 100 | AmerisourceBergen pricing | 10 |
| 2 | 100 | AmerisourceBergen reviews | 15 |
| 3 | 101 | AmerisourceBergen alternatives | 7 |
| 4 | 101 | buy AmerisourceBergen | 12 |
In this table, serves as the primary key. It uniquely identifies each ad and cannot be null.
is a foreign key that connects to the of the corresponding Google Ads campaign. This establishes a relationship between the ads and their campaigns, enabling easy querying to find which ads belong to a specific campaign or which campaigns a specific ad belongs to.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to link each ad to its ad group and the Google Ads account that the campaigns belong to, respectively.
SQL Question 8: Identify Customers with Specific Drug Purchase History
You are given a database that records all the purchases done by customers at AmerisourceBergen. Write an SQL query to find customers who have purchased drugs 'A' and 'B', but not 'C' during the year 2019.
Here are two sample tables for the problem:
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Samuel | Jackson |
| 4 | Alice | Johnson |
Example Input:
| order_id | customer_id | drug_name | order_date |
|---|---|---|---|
| 101 | 1 | A | 2019-06-08 |
| 102 | 1 | B | 2019-07-10 |
| 103 | 2 | A | 2019-06-18 |
| 104 | 2 | C | 2019-07-03 |
| 105 | 3 | B | 2019-09-22 |
| 106 | 3 | A | 2019-10-02 |
| 107 | 4 | C | 2019-11-06 |
Answer:
This query firstly checks whether there are orders from each customer who purchased 'A' and 'B' respectively, then it makes sure none of these customers have purchased 'C'. The and clauses ensure these conditions. We are also extracting the year from the using the function to ensure we're only looking at orders from the year 2019.
SQL Question 9: Average Cost Per Order for Each Vendor
AmerisourceBergen is a pharmaceutical sourcing and distribution services company. The company has been working with various vendors to distribute different pharmaceutical products. For managing their resources effectively, they need to know the average cost per order for each vendor.
This question will require you to use the function in SQL to determine what the mean cost per order is for each vendor AmerisourceBergen has worked with.
Here's the sample data for the problem:
Example Input:
| order_id | vendor_id | order_date | product_id | order_cost |
|---|---|---|---|---|
| 1141 | 12 | 06/08/2022 00:00:00 | 2001 | $100 |
| 2802 | 25 | 06/10/2022 00:00:00 | 4562 | $75 |
| 5299 | 36 | 06/18/2022 00:00:00 | 2001 | $115 |
| 3692 | 25 | 07/26/2022 00:00:00 | 4562 | $85 |
| 5127 | 12 | 07/05/2022 00:00:00 | 4562 | $90 |
Example Output:
| vendor_id | avg_order_cost |
|---|---|
| 12 | $95 |
| 25 | $80 |
| 36 | $115 |
Answer:
This SQL query works by grouping the data by , and then taking the average of the order cost for each group. The function is used to calculate the average cost, and the result is a table with each vendor and their corresponding average order cost.
To practice a very similar question try this interactive CVS Health Pharmacy Analytics (Part 1) Question which is similar for calculating profit related figures or this Alibaba Compressed Mean Question which is similar for using the AVG function.
SQL Question 10: What's a cross-join, and why are they used?
A cross-join, also known as a cartesian join, is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. This results in a table with a row for every possible combination of rows from the two input tables.
An example of when this might be useful is if you wanted to first make a dataset containing all possible pairs of customers and products data, in order to later build a Machine Learning model to predict the probability of a customer purchasing a particular product.
However, it is important to note that cross-joins can create very large tables, especially if the input tables have a large number of rows. For example, if you had 10,000 customers and 5,000 different product SKUs, the resulting cross-join would have 50 million rows.
SQL Question 11: Calculating Click-Through-Rates for AmerisourceBergen Digital Campaigns
AmerisourceBergen, as one of the leading drug wholesale companies, is interested in understanding click-through rates for its digital ad campaigns on various platforms. The goal is to understand the user interaction from viewing the ad, clicking on the ad to visit the product page, and then adding the product to the shopping cart. This process is captured in two tables. The table records every time a user views an ad with the columns: , , and . The table records every time a user clicks on an ad to see the product page with the columns: , , and .
Please write a SQL query to calculate the click-through rate per . The click-through rate is defined as the number of unique users who clicked on an ad divided by the number of unique users who viewed the ad, expressed as a percentage.
Example Input:
| view_id | user_id | ad_id | timestamp |
|---|---|---|---|
| 101 | 12 | 901 | 06/08/2022 00:00:00 |
| 102 | 14 | 902 | 06/10/2022 00:00:00 |
| 103 | 12 | 903 | 06/18/2022 00:00:00 |
| 104 | 15 | 901 | 07/26/2022 00:00:00 |
| 105 | 16 | 902 | 07/05/2022 00:00:00 |
Example Input:
| click_id | user_id | ad_id | timestamp |
|---|---|---|---|
| 201 | 14 | 902 | 06/18/2022 00:00:00 |
| 202 | 17 | 903 | 06/29/2022 00:00:00 |
| 203 | 16 | 902 | 07/10/2022 00:00:00 |
Answer:
In this solution, we perform a left join of the table with as our main table, using and as the key. This ensures we are capturing all users who viewed an ad, and if they clicked on one. We perform the calculation of click-through rate by counting distinct users who clicked (from the table) and divide by distinct users who viewed the ad (from the table), finally we multiply by 100 to express the result as a percentage.
To practice another question about calculating rates, try this TikTok SQL question within DataLemur's interactive coding environment:
How To Prepare for the AmerisourceBergen SQL Interview
The key to acing a AmerisourceBergen SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier AmerisourceBergen SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each DataLemur SQL question has multiple hints, full answers and best of all, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it graded.
To prep for the AmerisourceBergen SQL interview it is also a great idea to practice SQL questions from other healthcare and pharmaceutical companies like:
But if your SQL coding skills are weak, don't worry about going right into solving questions – go learn SQL with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL topics like filtering data with WHERE and RANK vs. DENSE RANK – both of which come up frequently during SQL job interviews at AmerisourceBergen.
AmerisourceBergen Data Science Interview Tips
What Do AmerisourceBergen Data Science Interviews Cover?
In addition to SQL query questions, the other question categories to prepare for the AmerisourceBergen Data Science Interview are:
- Statistics & Probability Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Questions centered on AmerisourceBergen culture and values
How To Prepare for AmerisourceBergen Data Science Interviews?
To prepare for AmerisourceBergen Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG & startups
- a refresher covering SQL, Product-Sense & ML
- over 1000+ 5-star reviews on Amazon
Don't forget about the behavioral interview – prepare for that with this list of behavioral interview questions for Data Scientists.