# 11 Alleghany SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

Alleghany employees often use SQL for analyzing insurance claims data for trends and anomalies, including identifying fraudulent claims activity, as well as managing large datasets for company-wide business intelligence applications, such as enterprise reporting and data visualization. Because of this, Alleghany frequently asks SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.

So, to help you practice for the Alleghany SQL interview, we've collected 11 Alleghany SQL interview questions – can you solve them?

## 11 Alleghany SQL Interview Questions

### SQL Question 1: Calculating the Average Monthly Sales per Product

Alleghany is a large American investment holding company that has interests in insurance, rail transport, and other business sectors. As part of the analytical team, you are asked to analyze the company's sales data.

Given the table as below:

##### Table Example Input:
sale_idproduct_idsale_dateprice
21341201/08/2022500
35671501/08/2022550
46871202/08/2022500
65781507/08/2022575
87461712/08/2022625

Write a SQL query that calculates the average monthly sales per product. The result should include the month, the product ID and the average sales for this product in the given month.

##### Example Output:
monthproductavg_sales
812500
815562.5
817625

Assuming that the is stored as TEXT in the format dd/mm/yyyy, we first need to extract the month from the . PostgreSQL has a function for this, which we can then combine with the function to get the month. We can then use window function to calculate the average price.

This query extracts the month from the and assigns each row to a partition based on and the extracted month. The function is then applied to each of these partitions to compute the average price per product for each month.

Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur

### SQL Question 2: Top 3 Department Salaries

Assume there was a table of Alleghany employee salary data. Write a SQL query to find the top 3 highest paid employees in each department.

#### Alleghany Example Input:

employee_idnamesalarydepartment_id
1Emma Thompson38001
2Daniel Rodriguez22301
3Olivia Smith20001
4Noah Johnson68002
5Sophia Martinez17501
8William Davis68002
10James Anderson40001

#### Example Input:

department_iddepartment_name
1Data Analytics
2Data Science

#### Example Output:

department_namenamesalary
Data AnalyticsJames Anderson4000
Data AnalyticsEmma Thompson3800
Data AnalyticsDaniel Rodriguez2230
Data ScienceNoah Johnson6800
Data ScienceWilliam Davis6800

You can solve this question 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 code above is confusing, you can find a detailed solution here: Top 3 Department Salaries.

### SQL Question 3: How does the LEAD() function differ from the LAG() function?

Both the and window functions are used to access a row at a specific offset from the current row.

However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.

Often, the offset for both functions is 1, which gives access to the immediately following/preceding row. Here's a SQL query example:

### SQL Question 4: Alleghany Insurance Claim Database Design

Alleghany Corporation is a large insurance conglomerate providing insurance services. This question focuses on designing a database for managing insurance claims within Alleghany. The important entities to consider include policyholders, policies, and claims.

• Policyholders can have multiple policies.
• Each policy can have multiple claims.
• Every claim is associated with a specific policy.

Create a database design for Alleghany to manage above scenario and provide SQL query to get all Policies for a specific PolicyHolder, along with all Claims associated with each policy.

##### Example Input:
PolicyHolder_idName
1John Doe
2Jane Smith
##### Example Input:
Policy_idPolicyHolder_idPolicy_Type
1001Health
1011Auto
1022Home
##### Example Input:
Claim_idPolicy_idClaim_FrameClaim_Description
200100Broken LegHospitalized
201101Auto AccidentCar Repair

This query joins the , , and tables to retrieve all policies for a specific policyholder, along with their associated claims. Here, is used because a policy might not have any associated claims. The clause filters the results for a specific . Be sure to replace with the actual you're interested in.

### SQL Question 5: How do you identify duplicated data in a table?

One creative way is to use the window function ROW_NUMBER() and partition by whichver columns you are loooking for duplicates in. If any row has a row_number() more than 1, then it's a duplicate!

You could also use COUNT(DISTINCT col1) vs. COUNT(*) and see if they return different numbers, indicating the prescence of duplicates!

### SQL Question 6: Filter Customers living in New York

Given the customer database containing details like customer_id, name, address, birth_date and annual_income, write a SQL query to filter out those customers in the age group between 20 and 30, living in New York, and having an annual income between $50,000 and$70,000.

##### Example Input:
101John Doe123 Elm street, New York, NY1992-03-24$60,000 102Jane Doe234 Bond street, Miami, FL1995-04-05$80,000
103Mary Johnson456 John street, New York, NY1990-09-11$65,000 104Samuel Davis343 Madison ave, New York, NY2001-06-23$35,000
105James Smith567 Pine street, Los Angeles, CA1980-07-12$120,000 #### Answer: The PostgreSQL query to solve the question would look like this: This SQL query will filter out the records from the table by applying multiple conditions with 'AND'. It uses the 'LIKE' operator together with the wildcard - '%' to filter out customers living in 'New York, NY'. The 'BETWEEN' operator is used to filter out customers in the age group of 20-30 years (you will have to adjust the date range according to the current date) and having annual income between $50,000 and$70,000. ### SQL Question 7: What is database denormalization, and when is it a good idea to consider it? Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms). A few reasons to denormalize a database: • Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases at Alleghany, as joins can be expensive and slow. • Scalability: Denormalization can be like a turbo boost for your database's scalability! By requiring less data to be read and processed when executing a query, denormalization can help your database handle a ton of queries without breaking a sweat. • Simplification: One way to simplify the design of a database is by using denormalization to reduce the number of tables and relationships that need to be managed. This can make the database easier to understand and maintain. Of course, don't over-do the database denormalization magic – too much redundancy can increase data storage costs, and get complicated to manage if there's frequent commands that need to be run. ### SQL Question 8: Average Claim Amount per Department Assuming that Alleghany is an insurance company, you've been tasked to evaluate performance across different departments. Specifically, you want to find out the average claim value processed by each department within the last year. You have two tables, and . The table records information about each department in the company, including the unique department ID and the name of the department. The table contains information about each claim the company has received, including a unique claim ID, the amount of the claim, the department that processed the claim, and the date the claim was filed. ##### Example Input: department_iddepartment_name 1Auto Insurance 2Home Insurance 3Health Insurance 4Life Insurance ##### Example Input: claim_idclaim_amountdepartment_idclaim_date 1234$5,000101/08/2022
5678$2,000202/15/2022 9101$3,000103/10/2022
1213$6,000404/05/2022 1415$4,000305/07/2022

This query does a simple join between the 'claims' and 'departments' tables on the 'department_id' and calculates the average claim amount for each department over the last year. In the WHERE clause, we filter for claims that were filed within the last year from the current date. The INTERVAL '1 year' syntax is specific to PostgreSQL and other SQL dialects may have different syntax to achieve the same result.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring grouping and finding averages or this Amazon Average Review Ratings Question which is similar for calculating average values in each category.

### SQL Question 9: Average Insurance Claim Per Year

As a Data Analyst at Alleghany, an investment holding company with insurance operations. You are tasked to find out the average insurance claim amount per year for each insurance type. The company offers different types of insurance like Auto, Health, Life, Travel, and Business.

##### Example Input:
claim_idclient_idclaim_dateinsurance_typeclaim_amount
10239992018-02-14Auto5000
24517842019-10-20Health8000
39238882018-06-25Life4000
48556602019-02-14Travel2000
52117842018-05-14Auto3000
##### Example Output:
yearinsurance_typeavg_claim_amount
2018Auto4000
2018Life4000
2019Health8000
2019Travel2000

This query uses the function to get the year part from the and the function to get the average for each year and . The clause is used to group the average claim amounts by year and insurance type. The clause is used to order the results by year and insurance type. The result of this query provides us with the average claim amount per year for each type of insurance, which can be useful for the company to assess the risk and profitability associated with each type of insurance.

### SQL Question 10: Can you explain what a cross-join is and the purpose of using them?

A cross-join, also known as a cartesian join, is like a mad scientist's laboratory experiment gone wild. It takes two tables and mixes them together to create a crazy new table with every possible combination of rows from the original tables.

Here's an example:

If you have 20 products and 10 colors, that's 200 rows right there! Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. Just like a mad scientist, use your powers wisely!

### SQL Question 11: Find Customers from a Specific City

As an analyst at Alleghany, you have been given a task to identify and sort all customers who live in the city of ‘New York’. The customers information is stored in a database table named ’ The table has the following columns: , , , , .

Please filter down the customers and find all records that match the city 'New York'.

##### Example Input:
customer_idfirst_namelast_nameemailcity
1JohnDoejohn.doe@example.comNew York
2JaneSmithjane.smith@example.comLos Angeles
3BillBrownbill.brown@example.comNew York
4SaraWhitesara.white@example.comChicago
5TomMooretom.moore@example.comNew York
##### Example Output:
customer_idfirst_namelast_nameemailcity
1JohnDoejohn.doe@example.comNew York
3BillBrownbill.brown@example.comNew York
5TomMooretom.moore@example.comNew York

This query uses the LIKE operator in the WHERE clause to filter out the customers from the city of 'New York'. The ORDER BY clause is used to sort the output by the customer_id.

### Preparing For The Alleghany SQL Interview

The key to acing a Alleghany SQL interview is to practice, practice, and then practice some more! Besides solving the above Alleghany SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Microsoft, Google, and Meta.

Each interview question has multiple hints, detailed solutions and most importantly, there's an online SQL coding environment so you can right online code up your query and have it executed.

To prep for the Alleghany SQL interview it is also a great idea to solve interview questions from other insurance companies like:

Get the latest news and updates from Alleghany and see how they're helping shape the future of financial services!

In case your SQL foundations are weak, don't worry about going right into solving questions – go learn SQL with this DataLemur SQL tutorial.

This tutorial covers SQL concepts such as LEAD/LAG window functions and INTERCEPT/EXCEPT – both of which show up frequently in Alleghany interviews.

### Alleghany Data Science Interview Tips

#### What Do Alleghany Data Science Interviews Cover?

Besides SQL interview questions, the other question categories covered in the Alleghany Data Science Interview are:

#### How To Prepare for Alleghany Data Science Interviews?

To prepare for the Alleghany Data Science interview make sure you have a deep understanding of the company's cultural values – this will be key to acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
• A Refresher covering Python, SQL & ML
• Amazing Reviews (1000+ reviews, 4.5-star rating)