LabCorp employees write SQL queries for analyzing medical test data to extract actionable insights that help healthcare providers make informed decisions about patient care. It is also used for managing patient databases for efficient information retrieval, ensuring that critical data is readily available when needed, this is the reason why LabCorp evaluates jobseekers with SQL query questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you prep, here’s 11 Laboratory Corp. of America SQL interview questions – able to answer them all?
LabCorp has different labs spread across the USA. Each lab tests different types of samples (COVID Tests, Blood Tests, etc). Each test has a result that can range between 1 to 10, where 10 is the best result.
Write a SQL query to find the average results of tests for each lab every month. Use a window function to provide this analysis. The result should be ordered by the lab number and then by the date.
test_id | lab_id | test_date | test_type | result |
---|---|---|---|---|
9102 | 302 | 2022-01-07 | COVID Test | 7 |
8100 | 205 | 2022-01-05 | Blood Test | 8 |
7133 | 302 | 2022-01-14 | COVID Test | 8 |
6510 | 119 | 2022-01-10 | Blood Test | 6 |
9420 | 205 | 2022-02-04 | COVID Test | 7 |
7812 | 119 | 2022-02-10 | Blood Test | 8 |
year | month | lab_id | avg_result |
---|---|---|---|
2022 | 1 | 119 | 6.00 |
2022 | 1 | 205 | 8.00 |
2022 | 1 | 302 | 7.50 |
2022 | 2 | 119 | 8.00 |
2022 | 2 | 205 | 7.00 |
In this query, we first prepare the year and month from the test_date field. We then average the result over each lab_id for each year and month. Finally, we order the result by lab_id, year, and month in ascending order. This Postgres query will help LabCorp understand the average performance of each lab on a monthly basis and will allow them to see any trends or anomalies.
To solve a similar window function interview problem which uses RANK() on DataLemur's free interactive coding environment, try this Amazon BI Engineer interview question:
Dive into Labcorp's focus on artificial intelligence in biopharma commercialization, revealing how they are using cutting-edge technology to enhance drug development processes! This knowledge can deepen your understanding of the intersection between AI and healthcare innovation.
Suppose there was a table of LabCorp employee salary data. Write a SQL query to find the top 3 highest paid employees in each department.
employee_id | name | salary | department_id |
---|---|---|---|
1 | Emma Thompson | 3800 | 1 |
2 | Daniel Rodriguez | 2230 | 1 |
3 | Olivia Smith | 2000 | 1 |
4 | Noah Johnson | 6800 | 2 |
5 | Sophia Martinez | 1750 | 1 |
8 | William Davis | 6800 | 2 |
10 | James Anderson | 4000 | 1 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
department_name | name | salary |
---|---|---|
Data Analytics | James Anderson | 4000 |
Data Analytics | Emma Thompson | 3800 |
Data Analytics | Daniel Rodriguez | 2230 |
Data Science | Noah Johnson | 6800 |
Data Science | William Davis | 6800 |
Code your solution to this problem interactively on DataLemur:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is tough, you can find a detailed solution with hints here: Top 3 Department Salaries.
To ensure the reliability and integrity of data, a database management system (DBMS) strives to maintain the ACID properties: Atomicity, Consistency, Isolation, and Durability. To illustrate these concepts, consider the following examples of how ACID properties apply to banking transactions:
LabCorp is a
The COALESCE() function returns the first non-NULL value from a list of values. This function is often used to replace a NULL with some default value, so that you can then take a or of some column without NULLs messing things up.
For example, suppose you ran a customer satisfaction survey for LabCorp and had statements like "I'd buy from LabCorp again". In the survey, customers would then answer how strongly they agreed with a statement on a scale of 1 to 5 (strongly disagree, disagree, neutral, agreee, strongly agree).
Because attention spans are short, many customers skipped many of the questions, and thus our survey data might be filled with NULLs:
customer_id | question_id | agree_scale |
---|---|---|
101 | 1 | 4 |
101 | 2 | 5 |
202 | 1 | 4 |
202 | 2 | NULL |
303 | 1 | 5 |
303 | 2 | NULL |
Before doing further analytics on this customer survey data, you could replace the NULLs in the column with the value of (because that corresponds to the default 'neutral' answer) using the function:
This would result in the following:
customer_id | question_id | agree_scale |
---|---|---|
101 | 1 | 4 |
101 | 2 | 5 |
202 | 1 | 4 |
202 | 2 | 3 |
303 | 1 | 5 |
303 | 2 | 3 |
LabCorp is a leading global life sciences company, which conducts various lab tests on a daily basis. From your lab test results history, calculate the average test result for each type of lab test.
test_id | patient_id | test_date | test_type | result |
---|---|---|---|---|
999 | 123 | 06/08/2022 00:00:00 | Blood Test | 5.3 |
934 | 456 | 06/10/2022 00:00:00 | Liver Test | 6.1 |
968 | 123 | 06/18/2022 00:00:00 | Liver Test | 7.6 |
842 | 192 | 07/26/2022 00:00:00 | Heart Test | 4.2 |
812 | 345 | 07/05/2022 00:00:00 | Blood Test | 6.0 |
test_type | avg_result |
---|---|
Blood Test | 5.65 |
Liver Test | 6.85 |
Heart Test | 4.2 |
This query groups the lab_tests table by the test_type and then takes the average of the result column for each group. The result is a table showcasing each test_type and the average result of that test.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages for each category or this Alibaba Compressed Mean Question which is similar for calculating mean values.
One way to find duplicate records in a table is by using , and then seeing which groups have more than one occurrence:
Another way is by using the operator:
LabCorp, a
LabCorp, a leading global life sciences company, maintains a database of customer records. These records store essential details such as the customer_id, name, email, and city of every customer. For this exercise, LabCorp would like to find all customers whose emails end with "@gmail.com" and reside in the city "Durham".
Here are some example records for your reference:
customer_id | name | city | |
---|---|---|---|
1001 | John Doe | john.doe@gmail.com | Durham |
1002 | Jane Smith | jane.smith@yahoo.com | Raleigh |
1003 | Mary Johnson | mary.johnson@gmail.com | Durham |
1004 | James Brown | james.brown@hotmail.com | Durham |
1005 | Patricia Davis | patricia.davis@gmail.com | Charlotte |
For this case, LabCorp wants a list of customer_ids who match the above criteria.
customer_id |
---|
1001 |
1003 |
This query filters the table "customers" by selecting the customer_id of every record where the email ends with '@gmail.com' (signifying Gmail users) and the city is 'Durham'. The percent sign (%) in the LIKE clause acts as a wildcard, matching any sequence of characters preceding '@gmail.com'. Consequently, this SQL query successfully identifies all customers from Durham who use Gmail.
In SQL, a value is NOT the same as a zero or a blank space. A NULL value represents the absence of a value or a missing value, whereas both zero and blank space are actually values.
To handle nulls, the function is often used to check for a null, and if there is a null, you can assign a default value.
Given two tables, one being containing customer data and another being containing order data for LabCorp, write a SQL query that will join these two tables to retrieve information about all customers and their associated orders.
Additionally, please return the data sorted by customer's last name in an ascending order.
Assume the following data in and tables:
customer_id | first_name | last_name | phone_number | |
---|---|---|---|---|
1001 | John | Doe | johndoe@email.com | 1234567890 |
1002 | Jane | Doe | janedoe@email.com | 0987654321 |
1003 | James | Smith | jamessmith@email.com | 1357924680 |
1004 | Jessica | Taylor | jessicataylor@email.com | 2468013579 |
order_id | customer_id | test_name | order_date | status |
---|---|---|---|---|
5001 | 1001 | Blood Test | 2022-06-01 | Complete |
5002 | 1002 | Urine Test | 2022-07-01 | In Progress |
5003 | 1003 | X-ray | 2022-08-01 | Complete |
5004 | 1001 | ECG | 2022-06-15 | Cancelled |
5005 | 1004 | Urine Test | 2022-09-01 | Complete |
The PostgreSQL query to solve the problem will be:
This query joins the table with the table on the basis of , i.e., the common column in the two tables. Then it selects all columns from the joined tables. Finally, the results are sorted in ascending order by the customer's last name. This way, a comprehensive detail of all customers and their respective orders is retrieved.
Because joins come up frequently during SQL interviews, practice this interactive Snapchat JOIN SQL interview question:
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. In addition to solving the above LabCorp SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Netflix, Google, and Amazon.
Each exercise has multiple hints, full answers and most importantly, there's an interactive SQL code editor so you can easily right in the browser your SQL query and have it graded.
To prep for the LabCorp SQL interview it is also helpful to practice SQL questions from other healthcare and pharmaceutical companies like:
However, if your SQL foundations are weak, forget about going right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers topics including LEAD/LAG and transforming strings with CONCAT()/LOWER()/TRIM() – both of which show up often in SQL job interviews at LabCorp.
Beyond writing SQL queries, the other question categories tested in the LabCorp Data Science Interview are:
To prepare for the LabCorp Data Science interview make sure you have a firm understanding of the company's culture and values – this will be key to acing the behavioral interview. For technical interviews prepare by reading Ace the Data Science Interview. The book's got: