8 Lincoln SQL Interview Questions (Updated 2024)
Data Analysts & Data Scientists at Lincoln write SQL queries to extract insights from insurance policy data, including policy coverage and customer demographics, for customer insights, as well as to manage financial transactions data, including premium payments and claims payouts, for fraud detection.. That is why Lincoln includes SQL questions during job interviews.
Thus, to help you study for the Lincoln SQL interview, we've collected 8 Lincoln National SQL interview questions in this article.
8 Lincoln National SQL Interview Questions
SQL Question 1: Identifying VIP Users for Lincoln
Consider the Lincoln company that specializes in shipping products to its customers. Lincoln management defines their VIP users as those customers who place orders more frequently than others. The more a customer places an order, the higher their importance. The task is to write a SQL query to identify the top 10 VIP customers within the last 12 months based on their frequency of orders.
Table Example:
| order_id | user_id | order_date | product_id | order_value |
|---|---|---|---|---|
| 1039 | 306 | 2021-11-14 | 20005 | $365.95 |
| 2054 | 512 | 2021-09-12 | 11560 | $124.75 |
| 3052 | 306 | 2021-10-06 | 20005 | $365.95 |
| 4051 | 105 | 2021-12-03 | 11560 | $124.75 |
| 5054 | 512 | 2022-02-12 | 87890 | $59.99 |
Example Output:
| user_id | order_count |
|---|---|
| 306 | 2 |
| 512 | 2 |
| 105 | 1 |
Answer:
This query uses over to count the number of orders placed by each user (). It filters records for the last 12 months using the operator. The clause groups the result-set by user_ids. The clause then sorts the result-set by in descending order, and the clause fetches only the top 10 records. Thus, the top 10 users with the highest frequency of orders within the last 12 months are returned.
To practice a similar customer analytics SQL question where you can solve it right in the browser and have your SQL solution automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: 2nd Highest Salary
Suppose there was a table of Lincoln employee salary data. Write a SQL query to find the 2nd highest salary at the company.
Lincoln Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Solve this problem directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution with hints here: 2nd Highest Salary.
SQL Question 3: What are the ACID properties in a DBMS?
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:
-
Atomicity: A transaction is either completed in full or not at all. For example, if a customer is transferring money between accounts, the transaction should either transfer the entire amount or none at all.
-
Consistency: A transaction is only allowed to complete if it follows all rules and constraints within the database. For example, if a customer is withdrawing money from an account, the transaction should only be allowed to proceed if there are sufficient funds available. Otherwise, the transaction is rejected.
-
Isolation: Concurrent transactions are kept separate from each other, so that the changes made by one transaction cannot be seen by another transaction until the first one is complete. This helps prevent conflicts, such as two customers attempting to withdraw money from the same account at the same time.
-
Durability: Once a transaction has been committed and completed, the changes made by the transaction are permanently stored in the database and will not be lost even if the database or system crashes. For example, if a customer makes a deposit, the transaction should be durable so that the deposit is not lost in the event of a system failure.
SQL Question 4: Calculate Monthly Average Ratings
Lincoln works at an online retail company, and has been tasked with analyzing product reviews. He needs to calculate monthly average ratings for each product. He has access to the table which includes past user ratings.
Can you help him write a SQL query that will help him calculate the average rating for each product on a monthly basis?
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2020-06-08 | 50001 | 4 |
| 7802 | 265 | 2020-06-10 | 69852 | 4 |
| 5293 | 362 | 2020-06-18 | 50001 | 3 |
| 6352 | 192 | 2020-07-26 | 69852 | 3 |
| 4517 | 981 | 2020-07-05 | 69852 | 2 |
Answer:
In this query, is used to group the reviews by month. in the clause allows us to specify the columns we want to group by. Finally, is used to calculate the average rating for each on a monthly basis. The clause at the end sorts the output by month and product_id.
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.5 |
| 6 | 69852 | 4.0 |
| 7 | 69852 | 2.5 |
To solve a similar window function interview problem which uses RANK() on DataLemur's free online SQL coding environment, try this Amazon SQL question asked in a BI Engineer interview:
SQL Question 5: Can you explain what an index is and the various types of indexes?
A database index is a data structure that improves the speed of data retrieval operations on a database table.
There are few different types of indexes that can be used in a database:
- Primary index: a unique identifier is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table.
- Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
- Clustered index: determines the physical order of the data in a table
For a concrete example, say you had a table of Lincoln customer payments with the following columns:
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values. This speeds up queries that filter or sort the data based on the , as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of June, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
SQL Question 6: Vehicle Sales Report
Assume you are an analyst working at the automobile company Lincoln. Your manager is particularly interested in the various car models sold in the last year. He wants you to present a report that shows sales per model for each month, sorted by the model with the highest total sales to the lowest. If two models have the same total quantity sold, order by model name in ascending order.
To perform this task, you have access to two tables: and .
The table has the following data:
Example Input:
| sales_id | model_id | sales_month | sales_year | quantity_sold |
|---|---|---|---|---|
| 1 | 1 | 01 | 2022 | 200 |
| 2 | 2 | 01 | 2022 | 150 |
| 3 | 1 | 02 | 2022 | 180 |
| 4 | 2 | 02 | 2022 | 100 |
| 5 | 3 | 01 | 2022 | 90 |
The table looks like this:
Example Input:
| model_id | model_name |
|---|---|
| 1 | Continental |
| 2 | Nautilus |
| 3 | Navigator |
Your task is to create a query that generates the following report:
Example Output:
| sales_month | model_name | total_sold |
|---|---|---|
| 01 | Continental | 200 |
| 01 | Nautilus | 150 |
| 01 | Navigator | 90 |
| 02 | Continental | 180 |
| 02 | Nautilus | 100 |
Answer:
With the above SQL query, the sales data by model per month is aggregated from the table. The WHERE condition specifies the sales year of interest. The data is then joined to the table using model_id as the key in order to obtain the correct model names for the output. Finally, the results are grouped by sales_month and model_name and then ordered by total_sales in descending order and then model_name in ascending order to meet the problem criteria.
SQL Question 7: What's a self-join, and when would you use one?
A self-join is a type of join in which a table is joined to itself. To perform a self-join, you need to specify the table name twice in the clause, and give each instance of the table a different alias. You can then join the two instances of the table using a clause, and use a clause to specify the relationship between the rows.
For example, say you were doing an HR analytics project and needed to analyze how much all Lincoln employees in the same department interact with each other. Here's a self-join query you could use to retrieve all pairs of Lincoln employees who work in the same department:
This query returns all pairs of Lincoln employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Lincoln employee being paired with themselves).
SQL Question 8: Average Employee Salary in Each Department
As a part of the HR team at Lincoln corporation, you are tasked with finding the average salary of employees in each department. In Lincoln corporation, each employee works in a single department.
Example Input:
| employee_id | first_name | last_name | department_id | salary |
|---|---|---|---|---|
| 1001 | John | Doe | 501 | 85000 |
| 1002 | Jane | Doe | 501 | 75000 |
| 1003 | Bob | Smith | 502 | 65000 |
| 1004 | Alice | Johnson | 502 | 70000 |
| 1005 | Charlie | Brown | 503 | 90000 |
Example Input:
| department_id | department_name |
|---|---|
| 501 | Human Resources |
| 502 | Engineering |
| 503 | Marketing |
Example Output:
| department_id | department_name | average_salary |
|---|---|---|
| 501 | Human Resources | 80000 |
| 502 | Engineering | 67500 |
| 503 | Marketing | 90000 |
Answer:
The above PostgreSQL query first joins the and tables on the field. It then groups the results by and . The function is used to calculate the average for each group, which effectively gives the average salary for each department. The result is selected and presented in a table with columns , and .
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for grouping and analyzing data or this Amazon Average Review Ratings Question which is similar for averaging and grouping data.
How To Prepare for the Lincoln SQL Interview
The best way to prepare for a Lincoln SQL interview is to practice, practice, practice. Besides solving the above Lincoln SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Netflix, Google, and Amazon.
Each problem on DataLemur has multiple hints, step-by-step solutions and best of all, there's an interactive coding environment so you can easily right in the browser your SQL query and have it executed.
To prep for the Lincoln SQL interview it is also helpful to practice interview questions from other insurance companies like:
Explore the latest news and insights from Lincoln and discover how they're leading the way in financial services!
In case your SQL foundations are weak, forget about jumping right into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers things like UNION vs. joins and creating summary stats with GROUP BY – both of which show up frequently in Lincoln SQL interviews.
Lincoln National Data Science Interview Tips
What Do Lincoln Data Science Interviews Cover?
In addition to SQL query questions, the other types of questions covered in the Lincoln Data Science Interview include:
- Stats Interview Questions
- Coding Questions in Python or R
- Product-Sense and Business Case Questions
- ML Modelling Questions
- Behavioral Interview Questions
How To Prepare for Lincoln Data Science Interviews?
I believe the optimal way to study for Lincoln Data Science interviews is to read the book Ace the Data Science Interview.
It solves 201 data interview questions sourced from tech companies like Netflix, Google, & Airbnb. The book's also got a refresher on Stats, ML, & Data Case Studies. And finally it's helped thousands of people land their dream job in data, 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 Lincoln behavioral interview. Start by understanding the company's values and mission.