# 11 Markel SQL Interview Questions (Updated 2024)

Updated on

June 30, 2024

Data Analytics, Data Science, and Data Engineering employees at Markel write SQL for analyzing insurance claims data and optimizing pricing models based on customer insights. That's why Markel usually asks SQL coding interview questions.

Thus, to help you prepare, we've collected 11 Markel SQL interview questions – can you answer each one?

## 11 Markel SQL Interview Questions

### SQL Question 1: Analyze Product Sales Over Time

In this question, you're asked to perform an analysis on the sales data for a hypothetical Markel product. Markel wants to know how their products are performing over time, and your job is to write an SQL query that will provide these insights.

Specifically, you're asked to report the total sales amount and sales count for each product by month in year 2022. Use the dataset provided below (sales), which contains sales transactions details for each product:

##### Example Input:
sales_idproduct_idsales_dateamount
2901200101/15/2022200.00
3201300102/05/2022180.00
4502200101/30/2022190.00
5303300103/25/2022150.00
3702500102/20/2022120.00

Markel also wants to know how much each product's sales differ from the average sales of all products.

We use the clause to separate out the data by product and month. The and functions aggregate the sales amount and count respectively. The function is a window function as it operates over a "window" of rows (in this case, partitioned by month) to compute the average sale over the same period. The result is a record of each product's sales count, total sales amount, average sales per month, and how much it differs from the average sales of all products over the same period.

To practice a similar window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:

### SQL Question 2: Top 3 Department Salaries

Imagine you had a table of Markel employee salary data. Write a SQL query to find the top 3 highest earning employees within each department.

#### Markel 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

Code your solution to 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 hard to understand, you can find a detailed solution here: Top 3 Department Salaries.

### SQL Question 3: What does the function do, and when would you use it?

The COALESCE() function returns the first non-NULL value from a list of values. This function is often used to replace a NULL with some default value, so that you can then take a or of some column without NULLs messing things up.

For example, suppose you ran a customer satisfaction survey for Markel and had statements like "I'd buy from Markel again". In the survey, customers would then answer how strongly they agreed with a statement on a scale of 1 to 5 (strongly disagree, disagree, neutral, agreee, strongly agree).

Because attention spans are short, many customers skipped many of the questions, and thus our survey data might be filled with NULLs:

customer_idquestion_idagree_scale
10114
10125
20214
2022NULL
30315
3032NULL

Before doing further analytics on this customer survey data, you could replace the NULLs in the column with the value of (because that corresponds to the default 'neutral' answer) using the function:

This would result in the following:

customer_idquestion_idagree_scale
10114
10125
20214
20223
30315
30323

### SQL Question 4: Database Design for Claims Management at Markel

Markel, an insurance company uses SQL database to manage claims data. Their insurance claim data includes information such as customer's details, policy information, the details of the incident, and how much has been paid out on the claim. Your task is to implement an SQL schema to store this complex set of data and then identify the customers with more than one claim in the same policy.

You may design and use the data structure that makes the most sense for the problem. Here's a suggested set of starting tables for this problem:

##### Example Input
101John Doe123 Main St
102Jane Smith456 Maple Ave
103Mike Johnson789 Oak St
##### Example Input
policy_idtype_of_insurancecustomer_id
1001Car101
1002Car102
1003Home103
##### Example Input
claim_idpolicy_idincident_detailamount_paid
20011001Car accident\$5000
20021001Car accident\$3000
20031002Car accident\$4000
20041003House fire\$10000
20051001Car accident\$2000

Write a PostgresSQL query to find customers who have submitted more than one claim for the same insurance policy. The result should contain customer name and the number of claims they have submitted per policy.

This query first joins the , , and tables on their appropriate foreign keys. It then groups the joined data by customer name and policy id, giving us the total counts of claims in each customer-policy pair. The clause then filters this grouped data to only include customer-policy pairs where the count is greater than 1, i.e., the customer has made more than one claim for the same policy. The results are subsequently ordered in descending order by count to see which customer-policy pairs have the highest claim rates.

### SQL Question 5: How does differ from just ?

Both and are used to combine the results of two or more SELECT statements into a single result set.

However, only includes one instance of a duplicate, whereas includes duplicates.

### SQL Question 6: Calculate Click-Through-Rates for Markel Digital Ads

You are given two tables, and . The table records each time a Markel digital ad is viewed, the table records each time an ad is clicked. Given these two tables, write a query to calculate the click-through rate for each ad.

##### Example Input:

This query first joins the two tables on the ad_id field, then it counts the number of impressions and clicks for each ad. By casting the count of clicks and impressions as float, we ensure that the division results in a decimal number and not an integer. The result is then multiplied by 100 to get the percentage click-through rate. The GROUP BY clause ensures the click-through rate is calculated for each unique ad_id.

To solve another question about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive coding environment:

### SQL Question 7: Can you describe the role of the constraint and provide an example of a situation where it might be applied?

The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.

You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.

For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.

### SQL Question 8: Average Total Claim Amount per Policy Type

Suppose Markel Corporation wants to know the average total claim amount for each policy type. They segment their policies into categories like 'Auto', 'Health', 'Life', 'Property' etc. They track the policy number, policy type, claim number and claim amount.

Their table is structured as follows:

##### Example Input:
policy_numpolicy_type
1001Auto
1002Health
1003Property
1004Life
1005Auto

And their table holds data like this:

##### Example Input:
claim_numpolicy_numclaim_amount
20011001500
200210023000
20031001250
200410031000
200510051800

Markel Corporation would like to see the data in this format:

##### Example Output:
policy_typeavg_claim_amount
Auto1083.33
Health3000.00
Property1000.00
Life0

The required query would be:

This query joins the and tables on and groups by . It calculates the average for each . If a policy type has no claims, COALESCE returns 0 instead of NULL.

### SQL Question 9: Filtering Customer Records Based on Specific String

As a data analyst for Markel, you are tasked with fetching certain customer records from the database. From the 'customers' table, find all customers whose first name starts with 'M' and the last name ends with 'son'.

The 'customers' table is structured as follows:

##### Example Input:
customer_idfirst_namelast_nameemailcountry
101MarianneJohnsonmarianne.johnson@example.comUSA
102DavidFergusondavid.ferguson@example.comUK
103MichaelJacksonmichael.jackson@example.comUSA
105MaggiePetersonmaggie.peterson@example.comAustralia

Your task is to write a SQL query which filters this table and returns only the records where the first name starts with 'M' and the last name ends with 'son'.

Here is a PostgreSQL query that would answer this:

This query filters the 'customers' table and selects records where the first_name field starts with 'M' and the last_name field ends with 'son'. The '%' is a wildcard character that matches any sequence of characters. 'M%' matches any string that starts with 'M' and '%son' matches any string that ends with 'son'.

The expected result is as follows:

##### Example Output:
customer_idfirst_namelast_nameemailcountry
101MarianneJohnsonmarianne.johnson@example.comUSA
103MichaelJacksonmichael.jackson@example.comUSA

### SQL Question 10: Can you describe the concept of database denormalization in layman's terms?

Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1NF, 2NF, 3NF, etc.).

This is typically done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.

Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with it's own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.

### SQL Question 11: Customer Purchase Analysis

Given the two tables below, and , write a query that joins these tables and returns customers' names, their purchase product ids, and the total amount spent per product.

The table has the following schema:

##### Example Input:
customer_idfirst_namelast_name
1JohnDoe
2JaneSmith
3JamesJohnson
4PatriciaWilliams
5RobertBrown

The table has the following schema:

##### Example Input:
purchase_idcustomer_idproduct_idamount
20113001100.00
20213002150.00
20323001200.00
20433002150.00
20543003150.00
20653001100.00
20753002150.00
20853003200.00

This query first joins the table with the table on the . Then, it uses a window function to calculate the total amount spent per product for each customer. The result is ordered by and to better display each customer's purchases. The output would provide each customer's name, the product they purchased, and the total amount they have spent on each product.

Since joins come up frequently during SQL interviews, try an interactive Spotify JOIN SQL question:

Before entering the interview make sure you read up on everything about the company, read the Markel about us page to help prepare.

### How To Prepare for the Markel SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Markel SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Markel SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.

Each DataLemur SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it executed.

To prep for the Markel SQL interview it is also helpful to practice SQL problems from other insurance companies like:

In case your SQL foundations are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.

This tutorial covers topics including CASE/WHEN/ELSE statements and AND/OR/NOT – both of these pop up often in Markel interviews.

### Markel Data Science Interview Tips

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

Besides SQL interview questions, the other types of questions to prepare for the Markel Data Science Interview are:

• Statistics and Probability Questions
• Python Pandas or R Coding Questions
• Analytics and Product-Metrics Questions
• Machine Learning Questions
• Behavioral Interview Questions

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

I'm sort of biased, but I think the best way to prepare for Markel Data Science interviews is to read the book I wrote: Ace the Data Science Interview.

The book covers 201 interview questions taken from Facebook, Google, & Amazon. It also has a crash course on Stats, SQL & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.