9 Stewart Title SQL Interview Questions (Updated 2024)
Stewart Title employees use SQL for analyzing title insurance claim trends, such as identifying patterns in claim frequencies and amounts, as well as optimizing property data management processes, including streamlining title search and exam workflows. That is why Stewart Title frequently asks SQL problems in interviews for Data Science and Data Engineering positions.
So, to help you prep, we've collected 9 Stewart Information Services SQL interview questions – can you answer each one?
9 Stewart Information Services SQL Interview Questions
SQL Question 1: Identify Top Customers for Stewart Title
Stewart Title is a company that provides comprehensive title insurance protection and professional settlement services for homebuyers and sellers, real estate agents and brokers, mortgage lenders, commercial property professionals, homebuilders, and developers.
Suppose you have access to a customers' database for Stewart Title, with a table named , and you are asked to identify the top 5 customers that have made the most transactions for the year 2022. Write an SQL query to select these customers.
The table is structured as follows:
Example Input:
| transaction_id | customer_id | transaction_date | property_id | value |
|---|---|---|---|---|
| 1024 | 200 | 01/08/2022 00:00:00 | 102 | 120000 |
| 1025 | 103 | 03/10/2022 00:00:00 | 103 | 200000 |
| 1026 | 200 | 04/12/2022 00:00:00 | 104 | 150000 |
| 1027 | 305 | 02/11/2022 00:00:00 | 105 | 300000 |
| 1028 | 103 | 05/01/2022 00:00:00 | 106 | 250000 |
Each record in the table represents a transaction action done by a customer.
The wanted output should look like:
Example Output:
| customer_id | transactions_count |
|---|---|
| 200 | 2 |
| 103 | 2 |
| 305 | 1 |
Answer:
Here is the SQL query using PostgreSQL:
This query filters the transactions for the year 2022. It groups the records by 'customer_id', counts the number of transactions per customer, and orders the results by the number of transactions in descending order (from the most transactions to the less). The clause ensures only the top 5 customers (VIP users) are returned. If there are customers with the same count of transactions, it sorts them by their IDs in ascending order.
To solve a similar power-user data analysis problem question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:
SQL Question 2: Department vs. Company Salary
You're given a table of Stewart Title employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Code your solution to this interview question and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.
SQL Question 3: What distinguishes an inner join from a full outer join?
An inner join returns only the rows that match the join condition between the two tables, whereas a full outer join returns all rows from both tables, including any unmatched rows.
Example:
Example:
SQL Question 4: Calculate Average Policy Value by Month
You have a table called of all Stewart Title insurance policies issued over a period of time. Each line in the table represents an individual policy issuance with the total value of the property insured, the policy issue date, the policy id and the agent id who sold the policy.
Write a SQL query that calculates the average value of policies issued each month by each agent.
Example Input:
| policy_id | agent_id | issue_date | value |
|---|---|---|---|
| 1011 | 321 | 2022-05-21 | 50000 |
| 1022 | 654 | 2022-05-15 | 75000 |
| 1033 | 321 | 2022-05-30 | 120000 |
| 1044 | 987 | 2022-06-05 | 55000 |
| 1055 | 654 | 2022-06-12 | 82000 |
| 1066 | 987 | 2022-06-20 | 62000 |
Example Output:
| month | agent | avg_value |
|---|---|---|
| 5 | 321 | 85000 |
| 5 | 654 | 75000 |
| 6 | 987 | 58500 |
| 6 | 654 | 82000 |
Answer:
This query works by first extracting the month from each policy's issue date. It then calculates the average value of all policies issued by each agent in each specific month using a window function. It partitions the data by , meaning it groups the data by each agent, and then orders that data by the month of . The function then calculates the average value of these partitions.
Finally, it orders the final result set by and . We use a window function here as it allows performing calculations across sets of rows that are related to the current row. In this scenario, it helps in getting the average per agent by month which wouldn't have been possible using a mere aggregate function.
To practice a similar window function interview problem which uses RANK() on DataLemur's free interactive SQL code editor, try this Amazon SQL question asked in a BI Engineer interview:
SQL Question 5: Can you explain the difference between a foreign and primary key in a database?
To explain the difference between a primary key and foreign key, let's inspect employee data from Stewart Title's HR database:
:
| employee_id | first_name | last_name | manager_id |
|---|---|---|---|
| 1 | Aubrey | Graham | 3 |
| 2 | Marshal | Mathers | 3 |
| 3 | Dwayne | Carter | 4 |
| 4 | Shawn | Carter |
In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.
could be a foreign key. It references the of the manager of each employee in the table, establishing a relationship between the employees and their managers. This foreign key allows you to easily query the table to find out who a specific employee's manager is, or to find out which employees report to a specific manager.
It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the department where each employee works, and the l of the location where each employee is based.
SQL Question 6: Insurance Policy Adjustment
Stewart Title, being an insurance company, has a series of policies that it works with. A business scenario frequently encountered is the need for policy adjustments. An adjustment may be due to anything from a policy upgrade to a policy cancellation, with each adjustment having a specific reason associated with it. It is critical to track these adjustments, their reasons, and their impacts on premium amounts.
You are tasked with modeling the tables needed to depict this scenario and subsequently writing a query that retrieves the total adjusted premiums for each policy type per month.
Example Input:
| policy_id | policy_type | start_date | premium_amount |
|---|---|---|---|
| 1 | Auto | 2022-01-01 | 1000 |
| 2 | Home | 2022-01-02 | 2000 |
| 3 | Health | 2022-01-03 | 3000 |
| 4 | Auto | 2022-02-01 | 1000 |
| 5 | Home | 2022-02-02 | 2000 |
Example Input:
| adjustment_id | policy_id | adjustment_date | adjustment_reason | adjustment_amount |
|---|---|---|---|---|
| 1 | 1 | 2022-03-01 | Upgrade | 200 |
| 2 | 2 | 2022-03-01 | Discount | -100 |
| 3 | 3 | 2022-02-01 | Upgrade | 300 |
| 4 | 4 | 2022-02-01 | Cancellation | -1000 |
| 5 | 5 | 2022-02-01 | Discount | -500 |
Answer:
In this query, we first summarize the policy adjustments by policy and month in the CTE. Then, we join the table with and calculate the adjusted premium as sum of original premium and total adjustments. Finally we order the result by policy_type and adjustment_month to make the output easier to understand.
SQL Question 7: In SQL, are blank spaces the same as a NULL?
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.
SQL Question 8: Find the Average Number of Policies Per Agent
Stewart Title is a title insurance company. As part of their services, they offer insurance policies to homebuyers and lenders. For this question, let's say the company wants to know the average number of policies handled by each agent to manage workload distribution.
Example Input:
| policy_id | agent_id | policy_date | policy_value | homebuyer_id |
|---|---|---|---|---|
| 1001 | 1 | 2022-06-08 | 50000 | 201 |
| 1002 | 2 | 2022-06-10 | 65000 | 202 |
| 1003 | 1 | 2022-06-18 | 55000 | 203 |
| 1004 | 3 | 2022-07-26 | 70000 | 204 |
| 1005 | 2 | 2022-07-05 | 80000 | 205 |
Example Output:
| agent_id | avg_policies |
|---|---|
| 1 | 2.0 |
| 2 | 2.0 |
| 3 | 1.0 |
Answer:
This PostgreSQL query first calculates the total number of policies per agent and then finds the average number of policies per agent. The subquery is used to create a temporary table whose rows contain the and the total number of policies they have. The outer query then calculates the average number of policies per agent from this table.
To practice a very similar question try this interactive LinkedIn Duplicate Job Listings Question which is similar for calculating duplicate entities or this Twitter Histogram of Tweets Question which is similar for calculating count per user.
SQL Question 9: Click-Through-Rates Analysis For Digital Ads
As a part of Stewart Title's online marketing team, your manager asks you to analyze the performance of their latest digital ads campaign. They specifically want to know the click-through-rates (CTR) for each ad. Click-through-rate is calculated as the number of ad clicks divided by the number of ad impressions, then multiplied by 100 for a percentage.
For this problem, suppose we have the below two tables: "impressions" and "clicks".
Example Data:
| ad_id | date | impressions |
|---|---|---|
| 1 | 10/26/2022 00:00:00 | 500 |
| 2 | 10/26/2022 00:00:00 | 600 |
| 1 | 10/27/2022 00:00:00 | 450 |
| 2 | 10/27/2022 00:00:00 | 550 |
| 3 | 10/27/2022 00:00:00 | 700 |
Example Data:
| ad_id | date | clicks |
|---|---|---|
| 1 | 10/26/2022 00:00:00 | 20 |
| 2 | 10/26/2022 00:00:00 | 30 |
| 1 | 10/27/2022 00:00:00 | 15 |
| 2 | 10/27/2022 00:00:00 | 25 |
| 3 | 10/27/2022 00:00:00 | 50 |
Answer:
The corresponding PostgreSQL query would look like this:
This query first joins the and tables based on both and . Then it calculates the CTR as the formula described above, ensuring to multiply by 100.0 to convert the result into a percentage. The final result will include the ad_id, date, number of impressions, number of clicks, and the calculated CTR for each row in the dataset.
Example Output:
| ad_id | date | impressions | clicks | CTR |
|---|---|---|---|---|
| 1 | 10/26/2022 00:00:00 | 500 | 20 | 4.0 |
| 2 | 10/26/2022 00:00:00 | 600 | 30 | 5.0 |
| 1 | 10/27/2022 00:00:00 | 450 | 15 | 3.33 |
| 2 | 10/27/2022 00:00:00 | 550 | 25 | 4.55 |
| 3 | 10/27/2022 00:00:00 | 700 | 50 | 7.14 |
To practice a similar SQL problem on DataLemur's free online SQL code editor, solve this Meta SQL interview question:
Stewart Title SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Stewart Title SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above Stewart Title SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like tech companies and insurance companies like Stewart Title.
Each problem on DataLemur has hints to guide you, full answers and most importantly, there's an interactive coding environment so you can instantly run your query and have it checked.
To prep for the Stewart Title SQL interview it is also useful to solve SQL questions from other insurance companies like:
- Voya SQL questions
- Sentry Insurance SQL questions
- American National SQL questions
- Arthur J. Gallagher SQL questions
Keep up with Stewart Title's latest news and announcements, driving innovation and growth in the industry!
In case your SQL query skills are weak, forget about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers topics including filtering strings using LIKE and window functions like RANK() and ROW_NUMBER() – both of these show up often during Stewart Title interviews.
Stewart Information Services Data Science Interview Tips
What Do Stewart Title Data Science Interviews Cover?
Besides SQL interview questions, the other question categories to prepare for the Stewart Title Data Science Interview are:
- Experimental Design & AB Testing Questions
- Python Pandas or R Coding Questions
- Analytics and Product-Metrics Questions
- Machine Learning Questions
- Behavioral Interview Questions based on Stewart Title company values
How To Prepare for Stewart Title Data Science Interviews?
To prepare for Stewart Title Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG, tech startups, and Wall Street
- a refresher covering Python, SQL & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't ignore the behavioral interview – prep for it using this guide on acing behavioral interviews.