10 Coherent SQL Interview Questions (Updated 2024)

Updated on

March 2, 2024

At Coherent, SQL does the heavy lifting for querying and managing laser technology data, and optimizing supply chain processes. Unsurprisingly this is why Coherent typically asks SQL questions during interviews for Data Analyst, Data Science, and BI jobs.

As such, to help you study for the Coherent SQL interview, we've collected 10 Coherent SQL interview questions – able to answer them all?

10 Coherent SQL Interview Questions

SQL Question 1: Identify Coherent's VIP Users

Coherent, Inc. is a company that sells software products. Their revenue is heavily dependent on the frequent purchases and usage of their products by specific users. These "VIP" users are integral to the company's success.

For this interview question, your task is to write a PostgreSQL query to analyze Coherent's customer database and identify these VIP users. Consider a VIP user as someone who has bought at least 10 different products and has an average product rating of at least 4.0.

Example Input:
Example Input:
Example Input:


This query first creates a table of each user's count of distinct product purchases and their average rating for the products. It then joins this with the original user table to output the user_id and usernames of VIP users based on the conditions provided.

To practice a related customer analytics question on DataLemur's free online SQL coding environment, try this Microsoft SQL Interview problem: Microsoft SQL Interview Question: Super Cloud Customer

SQL Question 2: Calculating Monthly Average Rating for Each Product

Coherent Inc., a company that sells various products, wants to understand their product performance in terms of customer reviews. You are given a table with fields , , , , . Write a SQL query to calculate the monthly average rating () for each .

The result should include the month (),, and average rating () for each month and product.

You can assume all dates in are in the format .

Example Input:
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522
Example Output:


Here’s a SQL query using PostgreSQL which will achieve the task:

This SQL query calculates the monthly average rating for each product. It makes use of the clause to calculate averages for each combination of month and product ID. The statement gets the month part from the field. We use the function to calculate the average of for the groups created by . The result is then ordered by month and product ID.

To practice another window function question on DataLemur's free interactive SQL code editor, try this Amazon SQL question asked in a BI Engineer interview: Amazon Window Function SQL Interview Problem

SQL Question 3: What are the similarities and differences between a clustered index and non-clustered index?

A clustered index is an index where the order of the rows in the database corresponds to the order of the rows in the index. Because of this, a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between the two is that the database tries to keep the data in the same order as the corresponding keys in the clustered index. This can improve the performance of most query operations, as it provides a linear-access path to the data stored in the database.

Coherent SQL Interview Questions

SQL Question 4: Designing a Database for Coherent's Business

Coherent is a company that specializes in producing and selling high power and precision lasers for commercial and scientific use. Given that the company is dealing with many products and customers, it's important to efficiently keep track of all the transactions, customers, and products' information.

Design a database schema for Coherent that contains the following entities: Customers, Products, and Sales.

The Customers table should contain each customer's id, name, and contact information. The Products table needs to track each product's id, name, and price. The Sales table will record each transaction, including the transaction id, customer id, product id, quantity, and the transaction date.

Provide this data in the form of markdown-formatted tables.

Example Input:
1001Robert Smithrobertsmith@gmail.com
1002Emma Johnsonemmajohnson@yahoo.com
1003Sophia Williamssophiawilliams@hotmail.com
Example Input:
2001Coherent E-10050000
2002Coherent L-50075000
2003Coherent N-30060000
Example Input:

Question: Write a SQL query to find out the total revenue that Coherent earned for each product in July 2021.


This query joins the Sales and Products tables on the product_id, then filters for transactions that occurred in July 2021. It then groups the results by product name (from the Products table) and calculates the total revenue for each product sold in July 2021 by multiplying the product's price by the quantity of the product sold (from the Sales table). The final result is a list of product names along with the total revenue for each product in July 2021.

SQL Question 5: Why would you use the SQL constraint?

The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.

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 6: Calculate the Average Employee Salary in Each Department

Coherent is a company that has several departments, each with employees earning different salaries. As an SQL developer, your task is to write a SQL query that will find the average salary for each department. Assume that there are two tables, "employees" and "departments", together holding all the necessary information.

Example Input:
Example Input:

Our task is to find the average salary of employees in each department.


Here is the PostgreSQL query to solve the problem:

The join operation combines rows from two tables (employees and departments) based on a related column (department_id). The GROUP BY clause is used with the aggregate function AVG to get the average salary of employees in each group (department_name).

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating stats within groups or this Facebook Average Review Ratings Question which is similar for calculating average values.

SQL Question 7: Can you explain the concept of a cross-join, and their purpose?

A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.

Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Coherent product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Coherent products.

Here's a cross-join query you could run:

Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Coherent had 500 different product SKUs, the resulting cross-join would have 5 million rows!

SQL Question 8: Average Monthly Laser Sales

Suppose Coherent is a company that specializes in the manufacturing and sales of lasers. Each sold laser has an associated serial number, price, and the date of sale recorded in the table. Coherent is interested in getting a report showing the average sales for each laser type on a monthly basis.

To achieve this, we will first need some example data:

Example Input:
12022-01-05Type A500
22022-01-12Type B600
32022-01-28Type A700
42022-02-03Type B800
52022-02-14Type A400

Coherent wants to find the average price per month for each type of laser. The output should resemble the following structure:

Example Output:
1Type A600
1Type B600
2Type A400
2Type B800


The solution to this problem involves using the command to group by both the month of the sale date and the type of laser. We can use the function in PostgreSQL to get the month from the . Furthermore, we should use the function to get the average price:

This SQL query will return one row for each unique combination of and . For each row, it will also return the average of all matching rows from the table.

SQL Question 9: Find All Customers With Email From Specific Domain

Coherent has a large database of customer records. The company is interested in identifying all customers whose email belongs to a specific domain, for example, "coherent.com". The table in the database has several fields including , , , , , and .

Please write an SQL query to filter out all customers with an email domain of "coherent.com".

Example Input:


This query will return all records from the table where the has "@coherent.com" as the domain. The "%" wildcard character in the LIKE clause is used to match any sequence of characters before "@coherent.com".

Example Output:

SQL Question 10: What does it mean for a DBMS to enforce atomicity, consistency, isolation, and durability?

A transaction is a one or more SQL commands which are executed as a singular unit if the transaction -commits- (or no execution hapens if the transaction -aborts-).

For transactions, a DBMS is supposed to enforce the follwing ACID properties: Atomicity, Consistency, Isolation, & Durability.

Here's what each one means:

  • Atomicity: the transaction is completed in an all-or-nothing way (no partial commits)
  • Consistency: the transaction is valid and follows all constraints and restrictions
  • Isolation: the transaction doesn't affect another transaction
  • Durability: the committed transactions is stored permanently in the DB (it doesn't dissapear!)

As you can see, it's pretty important for the multiple databases where Coherent store's it's data to be ACID-compliant!

Coherent SQL Interview Tips

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Coherent SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the above Coherent SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon. DataLemur Question Bank

Each interview question has hints to guide you, full answers and crucially, there is an online SQL coding environment so you can right in the browser run your SQL query answer and have it checked.

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

But if your SQL skills are weak, forget about diving straight into solving questions – refresh your SQL knowledge with this SQL interview tutorial.

Free SQL tutorial

This tutorial covers things like using wildcards with LIKE and aggregate functions – both of which show up often in Coherent SQL assessments.

Coherent Data Science Interview Tips

What Do Coherent Data Science Interviews Cover?

In addition to SQL interview questions, the other types of questions tested in the Coherent Data Science Interview are:

Coherent Data Scientist

How To Prepare for Coherent Data Science Interviews?

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