10 Molina Healthcare SQL Interview Questions (Updated 2024)
Molina Healthcare employees write SQL queries to analyze patient data, uncovering insights that help identify trends in patient health and treatment outcomes. They also use SQL to manage electronic health records to streamline access to patient information, ensuring compliance with healthcare regulations and enhancing the overall quality of patient care, that is why Molina Healthcare asks SQL questions in interviews for Data Science, Data Engineering, and Data Analytics jobs.
Thus, to help you prep, here’s 10 Molina Healthcare SQL interview questions – able to solve them?
10 Molina Healthcare SQL Interview Questions
SQL Question 1: Identify Top Spending Customers
As a data analyst for Molina Healthcare, you are asked to analyze patient data to determine which patients ("users") frequently utilize the most costly healthcare services. You are given access to a 'Services' database table which logs every service used by every user, including the service_id, user_id, date of the service, and cost of the service. Write a SQL query to identify the top 10 users who have the highest total service costs for the current year.
Example Input:
| service_id | user_id | service_date | cost |
|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 500 |
| 7802 | 265 | 2022-06-10 | 1000 |
| 5293 | 362 | 2022-06-18 | 400 |
| 6352 | 192 | 2022-07-26 | 1500 |
| 4517 | 981 | 2022-07-05 | 2000 |
Answer:
This SQL query first filters the services data to only include services used in the current year. Then, for each user, it sums up the cost of the services they used. The results are sorted in descending order by the total cost, and only the top 10 users (those with the highest total service cost) are returned.
To work on another SQL customer analytics question where you can solve it interactively and have your SQL solution automatically checked, try this Walmart SQL Interview Question:
Visit Molina Healthcare's news releases to learn about their recent developments and strategic initiatives in the healthcare sector! Following Molina Healthcare's updates can provide insights into how they are addressing the evolving needs of their members.
SQL Question 2: Department Salaries
Imagine you had a table of Molina Healthcare employee salary data, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary for March 2024. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Code your solution to this interview question directly within the browser on DataLemur:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.
SQL Question 3: Can you describe the meaning of a constraint in SQL in layman's terms?
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Molina 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 employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department 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 CHECK 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 DEFAULT constraint to set the employee hire date to the current date if no value is provided when a new employee is added to the database.
SQL Question 4: Calculate the average monthly insurance claim amount per member.
Given a database of insurance claims made by members of Molina Healthcare, write a SQL query to calculate the average monthly insurance claim amount made per member using a window function.
The database contains two tables. The first table, , contains member details with columns for and . The second table, , contains claims details with columns for , (timestamp) and (float).
Example Input:
| member_id | member_name |
|---|---|
| 001 | Jane Doe |
| 002 | John Smith |
| 003 | Mary Johnson |
Example Input:
| claim_id | member_id | claim_date | claim_amount |
|---|---|---|---|
| c001 | 001 | 2022-06-01 | 200.00 |
| c002 | 002 | 2022-06-15 | 150.00 |
| c003 | 003 | 2022-06-30 | 250.00 |
| c004 | 001 | 2022-07-01 | 300.00 |
| c005 | 002 | 2022-07-15 | 350.00 |
Your task is to calculate the average claim amount per member for each month.
Answer:
This query joins the and tables on to get the claim details along with member names. The window function is used to get the average claim amount per member for each month. The clause is used to calculate the average claim amount separately for each member and each month. The results are ordered by and for better readability.
Please make sure you have PostgreSQL version 9.0 or higher to use the window function. This question tests your understanding of window functions and your ability to analyse data over a set of related rows. These skills are important for a data analyst or scientist role at Molina Healthcare.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 5: In what circumstances might you choose to denormalize a database?
Normalization can reduce redundancy and improve performance and flexibility in a database.
By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Molina Healthcare's database to ever-changing business needs.
SQL Question 6: Filter Molina Healthcare Customers
Given a table of multiple Molina Healthcare customer’s data, how would you create a query that retrieves only those customers who are over 50 years old, live in California CA, and have a registered service plan?
Example Input:
| customer_id | age | state | service_plan |
|---|---|---|---|
| 1 | 35 | NY | Premium |
| 2 | 52 | CA | Basic |
| 3 | 60 | CA | Standard |
| 4 | 45 | TX | Premium |
| 5 | 55 | CA | Not Registered |
Example Output:
| customer_id | age | state | service_plan |
|---|---|---|---|
| 2 | 52 | CA | Basic |
| 3 | 60 | CA | Standard |
Answer:
The SQL query uses the clause to filter for Molina Healthcare customers who are over 50 (age > 50), live in California (state = 'CA'), and have a registered service plan (service_plan != 'Not Registered'). It then uses the command to retrieve all columns from the customers who satisfy these conditions.
SQL Question 7: Consider unique indexes, and non-unique indexes. What are some similarities and differences?
Some similarities between unique and non-unique indexes include:
- Both indexes improve the performance of SQL queries by providing a faster way to lookup the desired data.
- Both indexes use an additional data which requires more storage space which impacts write performance.
- Both indexes can be created on one or more columns of a table
Some differences between unique and non-unique indexes include:
- A unique index enforces the uniqueness of the indexed columns, meaning that no duplicate values are allowed in the indexed columns. A non-unique index allows duplicate values in the indexed columns.
- A unique index can be used to enforce the primary key of a table, but a non-unique index cannot.
- A unique index can have a maximum of one NULL value in the indexed columns, but a non-unique index can have multiple NULLs
SQL Question 8: Analyzing Click-Through-Rates for Molina Healthcare
Molina Healthcare conducts several digital campaigns to reach their potential customers. They track all the clicks and the process from the point a user visits their site and make an appointment with a healthcare provider.
A marketing analyst of Molina wants to analyze the click-through rates of these promotional campaigns. Specifically, they want to determine the conversion rate of users who clicked on their promotional ad and eventually booked an appointment.
Here is some sample data modeled around this scenario:
Example Input:
| click_id | user_id | click_date | campaign_id |
|---|---|---|---|
| 101 | 524 | 2022-06-01 09:00:00 | 1 |
| 102 | 378 | 2022-06-01 09:10:00 | 1 |
| 103 | 945 | 2022-06-01 09:15:00 | 1 |
| 104 | 769 | 2022-06-01 10:00:00 | 2 |
| 105 | 883 | 2022-06-01 10:10:00 | 2 |
Example Input:
| appointment_id | user_id | book_date |
|---|---|---|
| 501 | 524 | 2022-06-01 14:00:00 |
| 502 | 945 | 2022-06-02 10:00:00 |
| 503 | 378 | 2022-06-05 09:00:00 |
| 504 | 883 | 2022-06-07 13:00:00 |
| 505 | 323 | 2022-06-08 11:00:00 |
Answer:
This query works by joining the 'clicks' table to the 'appointments' table on the , where the is after the . For each campaign, it counts the total number of users who clicked, the unique users who booked an appointment, and calculates the conversion rate i.e., / . The COALESCE function is used to handle any NULL values introduced by the LEFT JOIN, and avoids divide-by-zero errors by defaulting to 0 if there are no matching bookings for a click.
To practice a similar problem on DataLemur's free online SQL code editor, solve this Meta SQL interview question:
SQL Question 9: Filter Customer Records for Specific Conditions
Assume you have a table called that stores detailed information about all the customers of Molina Healthcare. The table includes a column which contains information like the service they signed up for, their city of residence, etc. in a free text format.
Your task is to fetch all customer records where the includes information that the customer resides in 'Los Angeles' and has signed up for 'Medicaid' services.
The table appears as follows:
Example Input:
| customer_id | name | signup_date | description |
|---|---|---|---|
| 1234 | John Doe | 2021-01-15 | Lives in Houston. Signed up for Medicaid |
| 5678 | Jane Doe | 2021-02-20 | Lives in Los Angeles. Signed up for Medicare |
| 9123 | Mark Smith | 2022-05-10 | Lives in Los Angeles. Signed up for Medicaid |
| 4560 | Sarah Johnson | 2022-08-10 | Lives in Miami. Signed up for Private Insurance |
Your result should have all columns from the table for the matching conditions.
Answer:
In PostgreSQL, the query could be as follows:
This SQL command will filter the table and return all records where the field includes the string 'Los Angeles' and 'Medicaid'. The percent sign (%) here is a wildcard character that matches any sequence of characters. Hence, '%Los Angeles%' means any string that includes 'Los Angeles' and '%Medicaid%' means any string that includes 'Medicaid'.
The query will return all the matching records. In this case, only one record of Mark Smith matches the mentioned conditions. So, the result will have only one record.
SQL Question 10: What's the difference between a correlated and non-correlated sub-query?
A correlated sub-query is one that is linked to the outer query and cannot be executed on its own. It uses the outer query to filter or transform data by referencing a column from the outer query, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
Molina Healthcare SQL Interview Tips
The key to acing a Molina Healthcare SQL interview is to practice, practice, and then practice some more! Besides solving the above Molina Healthcare SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like FAANG tech companies and tech startups.
Each interview question has hints to guide you, step-by-step solutions and most importantly, there is an online SQL code editor so you can easily right in the browser your SQL query and have it executed.
To prep for the Molina Healthcare SQL interview you can also be useful to solve SQL problems 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 DataLemur SQL tutorial.
This tutorial covers topics including WHERE vs. HAVING and WHERE with AND/OR/NOT – both of which show up routinely in Molina Healthcare SQL interviews.
Molina Healthcare Data Science Interview Tips
What Do Molina Healthcare Data Science Interviews Cover?
Besides SQL interview questions, the other topics covered in the Molina Healthcare Data Science Interview are:
- Statistics and Probability Questions
- Python or R Programming Questions
- Product Analytics Questions
- ML Modelling Questions
- Behavioral Based Interview Questions
How To Prepare for Molina Healthcare Data Science Interviews?
I believe the optimal way to prepare for Molina Healthcare Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
It covers 201 data interview questions sourced from companies like Google, Tesla, & Goldman Sachs. The book's also got a crash course on Product Analytics, SQL & ML. And finally it's vouched for by the data community, which is how it's gotten to be an Amazon best-seller with over 1000+ 5-star reviews.
While the book is more technical, it's also crucial to prepare for the Molina Healthcare behavioral interview. Start by reading the company's culture and values.