9 AMN Healthcare SQL Interview Questions (Updated 2024)
Data Analysts and Data Engineers at AMN Healthcare write SQL queries for analyzing large volumes of healthcare data, helping them find useful insights that can improve patient care. They also use SQL to organize patient-care databases, making it quicker and easier to access important information when needed, that is why AMN Healthcare asks jobseekers SQL interview questions.
So, to help you prepare, we've curated 9 AMN Healthcare Services SQL interview questions – able to answer them all?
9 AMN Healthcare Services SQL Interview Questions
SQL Question 1: Analyzing Monthly Department Workloads in AMN Healthcare
Given a dataset containing information about health care tasks assigned to various departments in AMN Healthcare over various months, write a SQL query to calculate the monthly total tasks and the average task per department from the data set. Assume a task is finished on the same day it is assigned.
Please note that interview questions may vary based on the job requirements, this question is suitable for a data analyst position.
Example Input:
| task_id | department_id | task_date | status |
|---|---|---|---|
| 1001 | 1 | 2022-01-01 | Finished |
| 2002 | 2 | 2022-01-02 | Finished |
| 3003 | 1 | 2022-01-03 | In Progress |
| 4004 | 3 | 2022-02-01 | Finished |
| 5005 | 1 | 2022-02-02 | Finished |
| 6006 | 2 | 2022-02-03 | Finished |
| 7007 | 3 | 2022-02-04 | Finished |
| 8008 | 1 | 2022-03-01 | Finished |
| 9009 | 2 | 2022-03-02 | In Progress |
| 1010 | 3 | 2022-03-03 | Finished |
Answer:
This query extracts the month from the and groups by the then calculates the total tasks for each department each month. It subsequently calculates the average tasks per department using a window function which averages over the partition of . The clause filters out any task that is not marked as 'Finished'. The output will be ordered by the and for easier interpretation.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's online SQL coding environment:
.
Discover how AMN Healthcare is leveraging analytics to optimize workforce management and improve healthcare delivery! Gaining insights into their technological advancements can help you appreciate the role of data in enhancing healthcare services.
SQL Question 2: Top Department Salaries
Given a table of AMN Healthcare employee salary information, write a SQL query to find the top 3 highest earning employees within each department.
AMN Healthcare Example Input:
| 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 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| 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 |
Solve this interview question directly within the browser on DataLemur:
Answer:
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 code above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
SQL Question 3: What's a constraint in SQL, and do you have any examples?
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of AMN Healthcare employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the table, such as the employee's first and last name, cannot contain values.
UNIQUE: This constraint could be used to ensure that the is unique. This would prevent duplicate entries in the table.
PRIMARY KEY: This constraint could be used to combine the and constraints to create a primary key for the table. The could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the table and other tables in the database. For example, you could use a to link the to the in a table to track which department each employee belongs to.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a constraint to ensure that salary values are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a constraint to set the to the current date if no value is provided when a new employee is added to the database.
SQL Question 4: Healthcare Staffing Analysis
AMN Healthcare is a workforce solutions and staffing company. For their business, maintaining thorough and continually updated information of their registered healthcare professionals is crucial. Let's suppose there are two main entities here – Professionals and Hospitals. We want to maintain records of every job assignment of professionals in different hospitals along with their respective departments.
Database schema:
Example Input:
| professional_id | first_name | last_name | specialty |
|---|---|---|---|
| 101 | John | Doe | Nurse |
| 102 | Jane | Smith | Doctor |
| 103 | Bob | Brown | Technician |
Example Input:
| hospital_id | name | address |
|---|---|---|
| 201 | NY Central | 123 Central Ave, NY |
| 202 | City General | 543 General St, NY |
| 203 | County Health | 678 Health Ln, NY |
Example Input:
| assignment_id | professional_id | hospital_id | department | start_date | end_date |
|---|---|---|---|---|---|
| 301 | 101 | 201 | ICU | 2020-06-01 | 2020-12-31 |
| 302 | 102 | 202 | Pediatrics | 2021-01-01 | - |
| 303 | 103 | 203 | Emergency | 2021-01-01 | - |
Now, I may ask you a question like this:
Find out all the current assignments of Healthcare Professionals and their respective Hospitals, sorted by the start date of their assignments. For professionals with the end date as a null, consider their assignment as current.
Answer:
This query joins the , , and tables together to pull the relevant information about current assignments. It filters to only include assignments that are currently ongoing (i.e., is null) and sorts the results by , giving a detailed overview of each professional's current placement.
SQL Question 5: Can you explain 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.
Say for example you had sales analytics data from AMN Healthcare's CRM (customer-relationship management) tool.
The FOREIGN KEY constraint ensures that the data in the field of the "opportunities" table is valid, and prevents the insertion of rows in the table that do not have corresponding entries in the table. It also helps to enforce the relationship between the two tables and can be used to ensure that data is not deleted from the accounts table if there are still references to it in the table.
SQL Question 6: Average Hours of Assignments per Healthcare Professional
You work as a Data Analyst in AMN Healthcare, a leading provider of healthcare workforce solutions. Your manager asks you to analyze the average hours healthcare professionals in various fields work during their assignments to help in decision making regarding resourcing and managing work-life balance. Using the company's database, find out the average hours for each assignment type on a per healthcare professional basis.
example input:
| assignment_id | professional_id | assignment_type | hours_worked |
|---|---|---|---|
| 1001 | ABC100 | Nurse | 40 |
| 1002 | ABC100 | Nurse | 45 |
| 1003 | XYZ200 | Physician | 60 |
| 1004 | XYZ200 | Physician | 55 |
| 1005 | PQR300 | Phlebotomist | 32 |
| 1006 | PQR300 | Phlebotomist | 36 |
example output:
| professional_id | assignment_type | avg_hours |
|---|---|---|
| ABC100 | Nurse | 42.50 |
| XYZ200 | Physician | 57.50 |
| PQR300 | Phlebotomist | 34.00 |
Answer:
This PostgreSQL SQL query uses the function to compute the average hours worked per professional for their respective assignment types. It groups the results by the ID of the professional and the assignment type. The results are then sorted by average hours in descending order to show which professional type tends to work the most hours on average.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring data aggregation or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for involving analysis of product performance.
SQL Question 7: What are the similarities and difference between relational and NoSQL databases?
While knowing this answer is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at AMN Healthcare should be at least aware of SQL vs. NoSQL databases.
Relational databases and non-relational (NoSQL) databases have some key differences, particularly in terms of how data is stored. Whereas relational databases have tables, made up of rows and columns, NoSQL databases use various data models like:
- Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row within the same table
- Key-Value Stores – instead of rows and columns, you have keys, where each key is associated with only one value in a collection (similar to a Python dictionary data structure!)
- Graph Stores – instead of rows of data, you have nodes, and then can also have edges between entities (much like a Graph Data Structure for those who've taken a Computer Science data structures & algorithms class)
This added flexibility makes NoSQL databases well-suited for handling non-tabular data or data with a constantly changing format. However, this flexibility comes at the cost of ACID compliance, which is a set of properties (atomic, consistent, isolated, and durable) that ensure the reliability and integrity of data in a database. While most relational databases are ACID-compliant, NoSQL databases may not provide the same level of guarantees.
SQL Question 8: Filtering Healthcare Worker Records
As a staffing company, AMN Healthcare is constantly striving to keep track of all its healthcare professionals. From your understanding of the SQL keyword, they would like you to retrieve all healthcare worker records whose first names begin with 'J'.
Consider the following sample healthcare workers dataset.
Example Input:
| worker_id | first_name | last_name | designation | assignment_location | years_of_experience |
|---|---|---|---|---|---|
| 100 | John | Doe | Nurse | San Diego | 6 |
| 101 | Jacob | Smith | Doctor | San Francisco | 10 |
| 102 | Delilah | Johnson | Nurse Practitioner | Los Angeles | 8 |
| 103 | Katie | Brown | Physician | San Diego | 7 |
| 104 | Justin | Taylor | Registered Nurse | San Francisco | 5 |
The task is to filter these worker records and return only those records where the first name begins with a 'J'.
Answer:
You can achieve this using the SQL keyword combined with the wildcard character '%'. Here is the SQL query that will solve the problem:
This SQL query will return all healthcare worker records from the table whose first names begin with 'J'. In our sample dataset, it will return the records for John Doe, Jacob Smith, and Justin Taylor.
Example Output:
| worker_id | first_name | last_name | designation | assignment_location | years_of_experience |
|---|---|---|---|---|---|
| 100 | John | Doe | Nurse | San Diego | 6 |
| 101 | Jacob | Smith | Doctor | San Francisco | 10 |
| 104 | Justin | Taylor | Registered Nurse | San Francisco | 5 |
SQL Question 9: Calculating the Adjusted Screening Rating
AMN Healthcare arranges screening tests for its prospective healthcare professionals. The initial screening score lies between 0-100, where 0 means not at all eligible and 100 means highly eligible.
AMN Healthcare has hired a well-renowned statistician who suggests an adjustment to the screening scores using the following formula:
To evaluate the statistician's suggestion, AMN Healthcare decides to apply this formula on the records of its previously screened professionals.
Create a SQL query that calculates the adjusted screening score for every healthcare professional in the given assessments table:
Sample Input:
| assessment_id | professional_id | assessment_date | score |
|---|---|---|---|
| 927 | 101 | 2022-05-04 | 89 |
| 637 | 202 | 2022-05-14 | 78 |
| 128 | 301 | 2022-06-22 | 45 |
| 541 | 404 | 2022-07-08 | 92 |
| 698 | 505 | 2022-07-19 | 56 |
Answer:
This SQL query applies the given formula on the scores of each professional in the table and aliases the result as . The function ensures the result is a positive number, rounds it to the nearest integer, and apply the square root and square accordingly, multiplying the square root by 15. Finally, ensures this adjusted score remains within 0 and 100.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculation with rounding or this Alibaba Compressed Mode Question which is similar for operations on scores.
Preparing For The AMN Healthcare SQL Interview
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. Besides solving the above AMN Healthcare SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each DataLemur SQL question has multiple hints, full answers and crucially, there's an interactive coding environment so you can right in the browser run your SQL query answer and have it graded.
To prep for the AMN Healthcare SQL interview it is also useful to solve SQL problems from other healthcare and pharmaceutical companies like:
- Catalent SQL questions
- Bright HealthCare SQL questions
- Dentsply Sirona SQL questions
- Covetrus SQL questions
But if your SQL foundations are weak, forget about going right into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers SQL topics like filtering data with WHERE and filtering data with boolean operators – both of these show up frequently in AMN Healthcare SQL assessments.
AMN Healthcare Services Data Science Interview Tips
What Do AMN Healthcare Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions to practice for the AMN Healthcare Data Science Interview are:
- Statistics and Probability Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Interview Questions focussed on AMN Healthcare cultural values
How To Prepare for AMN Healthcare Data Science Interviews?
To prepare for AMN Healthcare Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Facebook, Google, & Amazon
- a crash course on SQL, AB Testing & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Also focus on the behavioral interview – prep for that with this Behavioral Interview Guide for Data Scientists.