# 11 Paycor SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At Paycor, SQL does the heavy lifting for extracting insights from large databases to improve HR and payroll processes, and optimizing data queries to enhance software performance. That's why Paycor LOVES to ask SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.

So, to help you ace the Paycor SQL interview, we've collected 11 Paycor SQL interview questions – can you solve them?

## 11 Paycor SQL Interview Questions

### SQL Question 1: Month wise average salary using SQL window function

You are given a table of Paycor in which each row is an employee's salary record with columns , , (the month in which the salary was provided), and .

Your task is to write a SQL query to find the average salary for each department each month.

##### Example Input:
emp_iddept_ids_monthssalary
110114000
210115000
310216000
410317000
510128000
610229000
7103210000
8101211000
##### Example Output:
monthdepartmentavg_salary
11014500.0
11026000.0
11037000.0
21019500.0
21029000.0
210310000.0

Using a window function, we can calculate the average salary by department for each month with the following SQL query:

In this query, is used to divide the result set into partitions or smaller sets. Here, it is grouped by and i.e., the month and the department id. The function is then applied to each of these partitions separately, calculating the average salary for each department for each month.

To solve a similar window function interview problem which uses RANK() on DataLemur's free online SQL coding environment, solve this Amazon SQL Interview Question:

### SQL Question 2: Payroll Management

Paycor is a company that provides solutions for managing payroll, human resources, and timekeeping. They are interested in understanding the average salary of their clients' employees based on different factors e.g., job role, department, and location. {#Question-2}

Assume Paycor has a table, an table, and a table. They want an SQL query that will return the average salary of each department, each job role, and each location.

The table looks like this:

##### Example Input:
department_iddepartment_namelocation
1MarketingNew York
2EngineeringSan Francisco
3SalesAustin

The table looks like this:

##### Example Input:
employee_idemployee_namejob_roledepartment_id
1John DoeProduct Manager1
2Jane SmithSoftware Engineer2
3Mike JohnsonSales Representative3

The table looks like this:

##### Example Input:
employee_idsalary
1100000
2150000
370000

This query joins the relevant tables together and performs a operation based on department name, job role, and location. It then averages the salaries () for each group. This will give Paycor the required insight into average salaries based on department, job role, and location.

### SQL Question 3: How do relational and non-relational databases differ?

A non-relational (NoSQL) database is any database that does not use the typical tabular format of rows and columns like in relational databases.

While knowing the four different types of NoSQL databases is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at Paycor should vaguely refresh these concepts:

• Document Databases – this database is designed for storing and querying retrieving document data (where each key is associated with a flexible document)
• Key-Value Stores – these databases uses keys where each key is associated with only one value in a collection (similar to a Python dictionary data structure!)
• Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row within the same table. Remember: "-With great flexibility comes great responsiblity-" – Batman if he was learning about NoSQL databases!
• Graph Stores – represents data in terms of the graph data structure, with nodes and edges between entities

### SQL Question 4: Calculating the Average Salary for each Department

Assuming you are working for Paycor, a Human Resource HR tech company, your job may encompass dealing with employee-related data. One SQL interview question could be, "What is the average salary for each department, sorted in descending order by the average salary?"

This question tests your ability to perform calculations on grouped data and order the results. Here’s the input and output sample:

#### Table:

Employee_IDFirstnameLastnameDepartment_IDSalary
1JohnDoe37000
2MaryJohnson38000
3JamesSmith15000
4PatriciaWilliams26000
5RobertBrown24000

#### Table:

Department_IDDepartment_Name
1HR
2Sales
3IT

#### Example Output:

Department_NameAverage_Salary
IT7500
Sales5000
HR5000

To answer the SQL interview question, you would write the following PostgreSQL query:

This PostgreSQL query joins the Employee table and the Department table on the Department_ID. Then, it groups the results by the Department_Name and calculates the average of the Salary column within each group. Finally, the results are sorted in descending order by the Average_Salary.

### SQL Question 5: What is the difference between cross join and natural join?

A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.

For example, say you worked on the Marketing Analytics team at Paycor, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).

Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:

A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.

For an example of each one, say you had sales data exported from Paycor's Salesforce CRM stored in a datawarehouse which had two tables: and .

An (which is a type of natural join) combines the two tables on the common

This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.

One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.

### SQL Question 6: Calculate the total payment received by each department monthly

As a data analyst at Paycor, a provider of cloud-based payroll and human resource software solutions, you are tasked with keeping track of the payments received every month by each department for using the software solutions. Given a table, your task is to write a SQL query that groups by the department and month to calculate the total amount paid. Consider the following example.

##### Example Input:
payment_iddepartment_idpayment_dateamount
10011001/06/2022500.00
10022001/06/2022200.00
10031002/06/2022600.00
10042006/06/2022400.00
10051003/07/2022500.00
10061001/07/2022200.00
10072002/07/2022400.00
10081002/08/2022500.00
##### Example Output:
monthdepartmenttotal_payment
6101100.00
620600.00
710700.00
720400.00
810500.00

Here is a SQL query that uses the GROUP BY clause paired with the SUM aggregate function to calculate the total payment received by each department monthly.

This query groups the data by the month of and the . It then calculates the total payment received by each department each month by using the SUM function on the field.

### SQL Question 7: When would you use denormalization?

Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.). There's a few reasons to denormalize a database:

Improved performance: Joins are slow AF, especially when dealing with the massive datasets that are typically used at Paycor. Denormalization can improve the performance of a database by reducing the number of joins that are required to retrieve data. This can be particularly useful when the database is being used for querying and reporting purposes, as joins can be expensive and slow.

Scalability: By reducing the amount of data that needs to be read and processed to execute a query, denormalization can enhance the scalability of a database. This can be useful when the database is anticipated to handle a large number of read-only queries (such as in OLAP use cases).

Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.

Because denormalization can create more complex update and delete operations, and pose potential data integrity issues, consider denormalization only if joins are causing performance bottlenecks.

### SQL Question 8: Find Customers With Email Domain

Paycor's customer database stores customer details including their email addresses. The management wants to focus their marketing campaigns on customers with a specific email domain, as they believe these customers may provide greater business opportunity. Write an SQL query that finds all customer records where the email field has an email with domain "@paycor.com".

##### Example Input:
customer_idfirst_namelast_nameemail
1001MichaelScottmichael.scott@paycor.com
1002JimHalpertjim.halpert@paper.com
1003PamBeeslypam.beesly@paycor.com
1004DwightSchrutedwight.schrute@farm.com
1005TobyFlendersontoby.flenderson@hr.com
##### Example Output:
customer_idfirst_namelast_nameemail
1001MichaelScottmichael.scott@paycor.com
1003PamBeeslypam.beesly@paycor.com

This SQL statement uses the keyword with the wildcard character () to match any customers whose email ends with "@paycor.com". It fetches all columns from the table where the value matches this pattern.

### SQL Question 9: Customer-Purchase Analysis

You are a data analyst at Paycor, asked to analyze the customer database and the company's purchases.

The table consists of customer details like , , and .

The table consists of purchase details like , , , and .

##### Example Input:
customer_idcustomer_namecustomer_emailcustomer_location
1John Doejohndoe@example.comNew York
2Jane Smithjanesmith@example.comCalifornia
3Robert Johnsonrobertj@example.comTexas
4Emily Davisemilydavis@example.comFlorida
5Michael Brownmichaelbrown@example.comIllinois
##### Example Input:
purchase_idcustomer_idproduct_idpurchase_datepurchase_amount
111012022-01-0540
221022022-02-1075
311032022-03-1560
431042022-04-2025
541052022-05-2550

Write a SQL query to find out the total amount spent by each customer, their last purchase date and location.

Order the results by the total amount spent in descending order.

This query joins the table with the table on the field. It then groups the results by , , and . Aggregate functions SUM and MAX are used to get the total purchase amount and the last purchase date for each customer, respectively. The final results are ordered by in descending order, showing customers who spent the most at the top.

Since joins come up so often during SQL interviews, practice this SQL join question from Spotify:

### SQL Question 10: What would you do to speed up a slow SQL query?

There's several steps you can take to troubleshoot a slow SQL query.

First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. After that, you can start changing your query, depending on what the source of the query performance issue is.

Generally, indexes can help speed up queries. Also de-normalizing your tables might help, to remove slow joins.

Lastly, you could always just upgrade your hardware! Time and money wasted improving query performance could just be better spent on mroe powerful database servers!

While this is a very surface-level approach to optimizing SQL query performance, it should do the trick for Data Analyst interviews and Data Science interviews at Paycor. Data Engineers should know a bit more about the and before the interview.

### SQL Question 11: Calculating Employee Performance Score

Paycor utilizes performance metrics to evaluate the productivity and effectiveness of their employees. The company has a special scoring system where they take into account the employee's working hours, their completed tasks, and the errors they made.

A hypothetical table contains the following data:

##### Example Input:
1401255
2451302
3471203
4501054
5481151

Your task is to calculate the performance score for each employee using the following formula:

Performance score = (Completed tasks / Working hours) * 100 - SQRT(Errors)

The result should be rounded to 2 decimal places.

##### Example Output:
employee_idperformance_score
1306.32
2287.08
3252.14
4210.00
5238.66

This query calculates a performance score for each employee in the table. To achieve this, it utilizes the SQL dividing operator (/) to compute the ratio of completed tasks to working hours and multiplies it by 100. The function is used to round the performance score up to 2 decimal points. The function computes the square root of errors and subtracts it from the earlier calculated value to get the final performance_score. The typecast is used to ensure that we don't lose precision during the division operation.

To practice a very similar question try this interactive Microsoft Teams Power Users Question which is similar for calculating highest productivity or this Stripe Repeated Payments Question which is similar for dealing with duplicate entries.

### Paycor SQL Interview Tips

The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the earlier Paycor SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each SQL question has hints to guide you, full answers and best of all, there's an interactive coding environment so you can right in the browser run your SQL query answer and have it checked.

To prep for the Paycor SQL interview you can also be useful to solve interview questions from other tech companies like:

But if your SQL query skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers topics including SQL joins with practice exercises and Subqueries – both of these show up often during Paycor interviews.

### Paycor Data Science Interview Tips

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

In addition to SQL query questions, the other question categories to practice for the Paycor Data Science Interview are:

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

The best way to prepare for Paycor Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from companies like Google, Tesla, & Goldman Sachs
• A Refresher covering SQL, Product-Sense & ML
• Great Reviews (900+ reviews, 4.5-star rating)