11 Quest Diagnostics SQL Interview Questions (Updated 2024)
Data Analytics, Data Science, and Data Engineering employees at Quest Diagnostics rely on SQL to analyze clinical laboratory data, uncovering trends and insights that can improve patient care. They also use SQL to manage patient data, ensuring that it is easily accessible while maintaining strict compliance with HIPAA regulations for security and privacy, which is why Quest Diagnostics frequently asks jobseekers SQL interview problems.
To help you practice for the Quest Diagnostics SQL interview, we've curated 11 Quest Diagnostics SQL interview questions in this blog.
11 Quest Diagnostics SQL Interview Questions
SQL Question 1: Identifying VIP Customers at Quest Diagnostics
Quest Diagnostics is a large medical diagnostics company that performs an enormous number of medical tests every day. Yet, there are certain customers - either doctors or healthcare facilities - who are considered 'power users' because they send in a large number of tests every week. Your task is to write a SQL query to identify these 'power users' for further business analysis and engagement.
For this analysis, we'll consider any customer who sends in more than 50 tests per week as a 'power user'.
The data is stored in two tables, and .
Example Input:
| customer_id | customer_name | customer_type |
|---|---|---|
| 1 | "Dr. Smith" | "Individual" |
| 2 | "City Hospital" | "Enterprise" |
| 3 | "County Clinic" | "Enterprise" |
| 4 | "Dr. Jones" | "Individual" |
| 5 | "State Medical Center" | "Enterprise" |
Example Input:
| test_id | customer_id | test_date | test_type |
|---|---|---|---|
| 1001 | 1 | 2022-06-01 | "COVID-19" |
| 1002 | 2 | 2022-06-01 | "Cholesterol" |
| 1003 | 1 | 2022-06-02 | "Blood Count" |
| 1004 | 3 | 2022-06-02 | "Lipid Profile" |
| 1005 | 2 | 2022-06-03 | "Liver Function" |
Answer:
This query joins the and tables on and counts the number of tests sent in by every customer in the past week. It then only returns the rows (i.e., customers) who sent in more than 50 tests in that timeframe. These are our 'power users'. The clause is used here because we are filtering on an aggregated value (i.e., ).
To practice a related super-user data analysis question on DataLemur's free online SQL coding environment, try this Microsoft Teams Power User SQL Interview Question:
Stay informed with the latest press releases from Quest Diagnostics, showcasing their commitment to improving patient care and advancing medical technology! Reading about their initiatives will help you understand the significant impact they have on the healthcare landscape.
SQL Question 2: Highly-Paid Employees
Assume you had a table of Quest Diagnostics employee salaries. Write a SQL query to find the employees who earn more than their direct manager.
Quest Diagnostics 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.
You can solve this interview question and run your code right in DataLemur's online SQL environment:
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 hard to understand, you can find a detailed solution here: Employees Earning More Than Managers.
SQL Question 3: Can you explain the purpose of the constraint and give an example of when you might use it?
The constraint is used to set rules for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the constraint, the operation will be unsuccessful.
For example, if you had Quest Diagnostics customer data, you could use a constraint to ensure that the email column contains only properly formatted email addresses or that the age column contains only positive integers. Here's an example of that:
SQL Question 4: Average Test Volume by Lab per Month
As a Data Analyst for Quest Diagnostics, you're asked to analzye lab data to understand trends in the volume of medical tests performed month by month over a year. Each lab can perform multiple tests, and you want to understand on average how many tests each lab is carrying out monthly, sorted by average tests in descending order.
Write a SQL query using PostgreSQL to calculate the average monthly volume of tests for each lab across the year.
Sample data is provided in the tables below:
Example Input:
| test_id | lab_id | test_date | test_name |
|---|---|---|---|
| 101 | 10 | 01/15/2021 | COVID19-ANTIBODY |
| 102 | 10 | 01/17/2021 | COVID19-PCR |
| 103 | 20 | 02/06/2021 | FLUCULTURE |
| 104 | 10 | 02/08/2021 | COVID19-ANTIBODY |
| 105 | 20 | 03/15/2021 | STREPCULTURE |
| 106 | 30 | 03/28/2021 | FLUCULTURE |
Example Input:
| lab_id | lab_name |
|---|---|
| 10 | Lab Alpha |
| 20 | Lab Beta |
| 30 | Lab Gamma |
Answer:
This query uses a window function to assess the volume of tests performed by each laboratory each month. Specifically, it counts the number of values for each combination of and month (indicated by the partitioning in the window function).
The result will be organized by in descending order, displaying the lab names (instead of the ID for more human-readable results), the month (extracted directly from the column), and the monthly test volume as calculated by the window function. This allows us to evaluate the labs' performance by their test volumes, giving a clearer picture of the workload distribution across labs.
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 5: Can you describe the difference between a unique and a non-unique index?
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
To define a unique index in PostgreSQL, you can use the following syntax:
To define a non-unique index in PostgreSQL, you can use the following syntax:
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
SQL Question 6: Diagnostics Test Data Analysis
Quest Diagnostics is a major player in the medical testing industry. Considering the company’s business model, one key aspect that is of interest is the tracking of tests done, the time it takes to process them, and the result of the tests. Suppose your job is to design a database to store this information and help in analyzing it.
Design a database with at least two tables - one for the tests done and another for the patient information. Indicate how you would denote the relationships between the tables and justify the choice of the data types for each of the columns.
Consider test cost and processing time as important factors in your design. Following is an example of how your base tables might look.
Example Input:
| patient_id | first_name | last_name | date_of_birth | gender |
|---|---|---|---|---|
| 001 | John | Doe | 01/01/1990 | Male |
| 002 | Mary | Jane | 02/02/1980 | Female |
| 003 | Peter | Parker | 03/03/1970 | Male |
| 004 | Tony | Stark | 04/04/1960 | Male |
| 005 | Bruce | Wayne | 05/05/1950 | Male |
Example Input:
| test_id | patient_id | test_name | cost | test_date | result_date | result |
|---|---|---|---|---|---|---|
| 0001 | 001 | CBC | 50.00 | 10/01/2022 09:00:00 | 10/02/2022 15:00:00 | Normal |
| 0002 | 002 | Urine Analysis | 30.00 | 10/02/2022 10:00:00 | 10/03/2022 17:00:00 | Abnormal |
| 0003 | 003 | Blood Glucose | 40.00 | 10/03/2022 11:00:00 | 10/04/2022 16:00:00 | Normal |
| 0004 | 004 | Cholesterol | 60.00 | 10/04/2022 12:00:00 | 10/05/2022 13:00:00 | High |
| 0005 | 005 | Liver function | 70.00 | 10/05/2022 08:00:00 | 10/06/2022 14:00:00 | Normal |
Given the above tables, can you write a query that will provide the average cost and processing time in hours of tests based on their result type?
Answer:
Using PostgreSQL, your solution may look similar to the below:
This PostgreSQL query will group the test results by their type (i.e., "Normal", "Abnormal", and "High"). It will then calculate the average cost and processing time in hours per test result by finding the time difference between the test and result dates in hours. On average, this will give an idea of how long a certain type of test result takes to process along with its cost.
SQL Question 7: What's the difference between a one-to-one vs. a one-to-many relationship between two entities? Give examples.
In database schema design, a one-to-one relationship between two entities is where each entity is associated with only one instance of the other entity. For example, the relationship between a car and a license plate is one-to-one, because each car can only have one licensce plate, and each licensce plate belongs to exactly one car.
On the other hand, a one-to-many relationship is where one entity can be associated with multiple instances of the 2nd entity. For example, a teacher can teach multiple classes, but each class is associated with only one teacher.
SQL Question 8: Filter Customer Records
Quest Diagnostics is a leading provider of diagnostic information services. Your task is to write a query to filter the customer records based on multiple conditions: customers who made an appointment after January 1, 2020, got tested for COVID-19, and whose results were positive.
Example Input:
| appointment_id | customer_id | appointment_date | test_type |
|---|---|---|---|
| 1001 | 8501 | 01/15/2020 | COVID-19 |
| 1002 | 8502 | 03/17/2020 | COVID-19 |
| 1003 | 8503 | 02/20/2020 | Influenza |
| 1004 | 8504 | 05/27/2020 | COVID-19 |
| 1005 | 8505 | 12/26/2019 | COVID-19 |
Example Input:
| appointment_id | result |
|---|---|
| 1001 | Positive |
| 1002 | Negative |
| 1003 | Positive |
| 1004 | Positive |
| 1005 | Positive |
Example Output:
| customer_id | appointment_date | result |
|---|---|---|
| 8501 | 01/15/2020 | Positive |
| 8504 | 05/27/2020 | Positive |
Answer:
This SQL query joins the and tables on the field. The query then filters the records for appointments made after January 1, 2020, where the test type was 'COVID-19' and the result was 'Positive'. The result set includes the , the date of the appointment, and the test result.
SQL Question 9: Average Test Result Score
At Quest Diagnostics, you are tasked with finding the average test score of all medical tests done in each department.
Below is a table that contains some mock data for you to practice with:
Example Input Table:
| test_id | department_id | test_date | patient_id | score |
|---|---|---|---|---|
| 1 | 2 | 01/08/2022 00:00:00 | 5 | 85 |
| 2 | 3 | 02/08/2022 00:00:00 | 6 | 90 |
| 3 | 2 | 03/08/2022 00:00:00 | 7 | 88 |
| 4 | 1 | 04/08/2022 00:00:00 | 8 | 76 |
| 5 | 3 | 05/08/2022 00:00:00 | 9 | 91 |
Example Output:
| department_id | average_score |
|---|---|
| 1 | 76.00 |
| 2 | 86.50 |
| 3 | 90.50 |
Answer:
This PostgreSQL query uses the function to find the average test score by department. It groups the rows in the table by and then calculates the average score of the tests in each department. For each unique department present in the table, it will output one row in the result set with the and the average test score.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculation of averages for grouped data or this Alibaba Compressed Mean Question which is similar for finding mean values in a dataset.
SQL Question 10: What are the similarities and differences between a clustered index and non-clustered index?
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
SQL Question 11: Find the Average Number of Tests and Their Types Ordered by Clinics in a Month
As an analyst at Quest Diagnostics, you are tasked with understanding the tendencies of clinics to order specific types of tests. Write an SQL query to find the average number of diagnostic tests of each type that are ordered by clinics each month.
Example Input:
| order_id | clinic_id | test_id | order_date |
|---|---|---|---|
| 1001 | 201 | 3001 | 07/01/2022 |
| 1002 | 203 | 3002 | 07/05/2022 |
| 1003 | 202 | 3001 | 07/10/2022 |
| 1004 | 201 | 3001 | 07/15/2022 |
| 1005 | 203 | 3002 | 07/20/2022 |
| 1006 | 202 | 3002 | 08/01/2022 |
| 1007 | 201 | 3001 | 08/10/2022 |
| 1008 | 203 | 3002 | 08/15/2022 |
Example Input:
| test_id | test_type |
|---|---|
| 3001 | Blood |
| 3002 | Urine |
Example Output:
| month | test_type | avg_orders |
|---|---|---|
| 7 | Blood | 1.5 |
| 7 | Urine | 2 |
| 8 | Blood | 1 |
| 8 | Urine | 1 |
Answer:
In this query, we first create a subquery that groups orders by month and . Then, we join this subquery with the table to get the test type. Finally, we aggregate on the month and to get the average orders.
Preparing For The Quest Diagnostics SQL Interview
The best way to prepare for a Quest Diagnostics SQL interview is to practice, practice, practice. In addition to solving the above Quest Diagnostics SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each SQL question has multiple hints, full answers and best of all, there is an interactive coding environment so you can easily right in the browser your query and have it executed.
To prep for the Quest Diagnostics SQL interview you can also be useful to solve SQL problems from other healthcare and pharmaceutical companies like:
- Biogen SQL questions
- DaVita SQL questions
- Boston Scientific SQL questions
- Community Health Systems SQL questions
But if your SQL query skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this interactive SQL tutorial.
This tutorial covers things like sorting results with ORDER BY and functions like SUM()/COUNT()/AVG() – both of these pop up frequently in Quest Diagnostics SQL interviews.
Quest Diagnostics Data Science Interview Tips
What Do Quest Diagnostics Data Science Interviews Cover?
In addition to SQL query questions, the other question categories covered in the Quest Diagnostics Data Science Interview are:
- Probability & Stats Questions
- Python or R Programming Questions
- Product-Sense and Business Case Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for Quest Diagnostics Data Science Interviews?
I believe the best way to study for Quest Diagnostics Data Science interviews is to read the book Ace the Data Science Interview.
It solves 201 interview questions sourced from Microsoft, Amazon & startups. The book's also got a crash course covering 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 important to prepare for the Quest Diagnostics behavioral interview. Start by understanding the company's values and mission.