9 Mu Sigma SQL Interview Questions (Updated 2024)
Data Analysts and Data Engineers at Mu Sigma uses SQL to extract data from various client databases by using subqueries and joins to combine data from multiple sources. They also use SQL to manage and manipulate big-data platforms by optimizing database performance, creating data pipelines, and ensuring data quality, which is why Mu Sigma often asks SQL interview questions.
Thus, to help you ace the Mu Sigma SQL interview, we'll cover 9 Mu Sigma SQL interview questions in this blog.
9 Mu Sigma SQL Interview Questions
SQL Question 1: Analyze Cumulative Monthly Sales Performance
Suppose you are a data analyst at Mu Sigma. You have been provided with a sales record for the last 12 months. Your task is to analyze product-wise cumulative monthly sales and to identify any product which has shown a monthly decrease in sales.
Below is a sample sales record data:
Example Input
| sales_id | product_id | sales_date | sales_amount |
|---|---|---|---|
| 1001 | 101 | 01/01/2022 | 1200.00 |
| 1002 | 102 | 01/10/2022 | 500.00 |
| 1003 | 103 | 01/25/2022 | 900.00 |
| 1004 | 101 | 02/05/2022 | 1000.00 |
| 1005 | 102 | 02/20/2022 | 800.00 |
| 1006 | 101 | 03/15/2022 | 1500.00 |
| 1007 | 103 | 03/20/2022 | 1000.00 |
| 1008 | 101 | 04/02/2022 | 1200.00 |
For the above dataset, your SQL query should return a table that shows product Id, month of sale, cumulative sales for the month, and an indicator showing if the sales decreased from last month.
Example Output
| product_id | sale_month | cumulative_sales | sales_decreased |
|---|---|---|---|
| 101 | 01 | 1200.00 | 0 |
| 101 | 02 | 1000.00 | 1 |
| 101 | 03 | 1500.00 | 0 |
| 101 | 04 | 1200.00 | 1 |
| 102 | 01 | 500.00 | 0 |
| 102 | 02 | 800.00 | 0 |
| 103 | 01 | 900.00 | 0 |
| 103 | 03 | 1000.00 | 0 |
Answer:
Above query first calculates product-wise cumulative sales for each month. Then it checks if the cumulative sales of a product decreased from the previous month and assigns an indicator accordingly. WINDOW function is used in both steps to perform column-level calculations based on the preceding rows within each product_id's window.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Second Highest Salary
Assume you had a table of Mu Sigma employee salary data. Write a SQL query to find the 2nd highest salary amongst all the .
Mu Sigma Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Check your SQL query for this question directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: What's a database view, and what's their purpose?
Database views are virtual tables based on the results of a SQL statement. They're just like vanilla tables, except views allow you to create simplified versions of tables or hide sensitive data from certain users.
In PostgreSQL, you can create a view by using the command. Here's an example for the table:
SQL Question 4: Product Handling and Order Management
Mu Sigma company is running a retail business and they want to keep track of their business analytics. The main entities of their business model consist of products, orders, and customers. They want to know about the total orders and the total amount a customer has purchased, considering the unit price of each product and quantity bought by the customer in each order.
To find this, you will need to create three tables: , and .
table:
| product_id | product_name | unit_price |
|---|---|---|
| 50001 | 'Product 1' | 20 |
| 69852 | 'Product 2' | 15 |
| 72218 | 'Product 3' | 30 |
table:
| customer_id | customer_name |
|---|---|
| 123 | 'John' |
| 265 | 'Sarah' |
| 362 | 'Jack' |
table:
| order_id | customer_id | product_id | quantity | order_date |
|---|---|---|---|---|
| 7865 | 123 | 50001 | 2 | '2022-08-15' |
| 8765 | 123 | 69852 | 1 | '2022-08-15' |
| 8654 | 265 | 72218 | 3 | '2022-08-20' |
| 8432 | 362 | 50001 | 2 | '2022-08-20' |
| 8506 | 123 | 72218 | 2 | '2022-08-25' |
Answer:
This SQL query is using joins to connect the three tables through their keys. It accumulates the total number of orders and the total amount transacted by every customer. By using aggregate functions in conjunction with a GROUP BY, the query gives an accurate view of the customers, their order counts, and the total amount they have transacted.
SQL Question 5: How do you determine which records in one table are not present in a second table?
To identify records in one table that do not appear in another, you can use a LEFT JOIN and examine NULL values in the right-side table.
Say for example you had exported Mu Sigma's CRM (Customer Relationship Management) database into PostgreSQL, and had a table of sales leads, and a second table of companies.
Here's an example of how a query could find all sales leads that are not associated with a company:
This query brings back all rows from the sales leads table, along with any matching rows from the companies table. If there is no matching row in the companies table, NULL values will be returned for all of the right table's columns.
We then filter out out any rows where the column is , leaving only the sales leads that are NOT associated with a company.
SQL Question 6: Filter Customer Data for Specific Criteria
Mu Sigma is a leading data analytics firm. For optimizing their services, they often need to generate insights from customer data. In this scenario, the company wants to filter their customer records based on three conditions:
- Customers who are of "premium" tier.
- Customers who have made more than 100 orders.
- Customers living in the city "Chicago".
Given the table below, write a SQL query that filters customer records based on the above conditions.
Example Input:
| user_id | username | membership_tier | number_of_orders | city |
|---|---|---|---|---|
| 1 | user_1 | basic | 80 | New York |
| 2 | user_2 | premium | 150 | Chicago |
| 3 | user_3 | premium | 350 | Boston |
| 4 | user_4 | premium | 120 | Chicago |
| 5 | user_5 | basic | 210 | Chicago |
Answer:
This SQL query filters the table to return rows where the is 'premium', the is greater than 100, and the is 'Chicago'.
Example Output:
| user_id | username | membership_tier | number_of_orders | city |
|---|---|---|---|---|
| 2 | user_2 | premium | 150 | Chicago |
| 4 | user_4 | premium | 120 | Chicago |
SQL Question 7: Could you describe a self-join and provide a scenario in which it would be used?
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
For instance, suppose you had website visitor data for Mu Sigma, exported from the company's Google Analytics account. To assist the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to generate all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair."
You could use the following self-join:
This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).
SQL Question 8: Average Duration of Client Engagements
As a data consultant at Mu Sigma, you are evaluating the performance of various project teams. Given a table containing , , , and , write a SQL query to find the average duration (in days) of engagements for each client.
Example Input:
| project_id | client_id | start_date | end_date |
|---|---|---|---|
| 1 | A | 01/01/2021 | 03/01/2021 |
| 2 | A | 04/01/2021 | 05/01/2021 |
| 3 | B | 01/01/2021 | 02/01/2021 |
| 4 | C | 05/01/2021 | 07/01/2021 |
| 5 | C | 08/01/2021 | 11/01/2021 |
Example Output:
| client_id | avg_duration |
|---|---|
| A | 60.00 |
| B | 30.00 |
| C | 75.00 |
Answer:
In this query, we first calculate the duration of each project by subtracting the from the for each row in the table. We then group our calculations by and find the average duration of projects for each client by using the AVG function on the durations. The result will show us, for each client, the average duration of their projects.
To practice a very similar question try this interactive Facebook Average Post Hiatus (Part 1) Question which is similar for calculating average duration with date data or this Amazon Average Review Ratings Question which is similar for aggregating and averaging data for each unique entity.
SQL Question 9: Analyze Click-Through and Conversion Rates
Mu Sigma relies heavily on its digital ads and online store to sell its data analytics products. In a particular month, the company displayed many ads to its users, and each ad, upon being clicked, would lead the user to a product's detail page. The user could then, if they chose, add the product to their cart. The exact chain of events was recorded in two tables, and .
In the table, there is a record for each ad clicked by a user. In the table, there is a record for each product added to a cart. All actions are tagged with the user_id for the user who performed the action, and the product_id for the product that was involved.
Use SQL to calculate the overall click-through-rate (the number of unique users who clicked on an ad divided by the number of unique users who saw an ad), and the conversion rate (the number of unique users who added a product to the cart divided by the number of unique users who clicked on an ad).
Example Input:
| click_id | user_id | click_date | product_id |
|---|---|---|---|
| 6281 | 321 | 06/14/2021 00:00:00 | 50007 |
| 7901 | 567 | 06/15/2021 00:00:00 | 69856 |
| 4536 | 692 | 06/18/2021 00:00:00 | 50001 |
| 7460 | 987 | 07/26/2021 00:00:00 | 69862 |
| 3902 | 456 | 07/05/2021 00:00:00 | 69857 |
Example Input:
| cart_id | user_id | add_date | product_id |
|---|---|---|---|
| 6710 | 321 | 06/15/2021 00:00:00 | 50007 |
| 7825 | 692 | 06/19/2021 00:00:00 | 50001 |
| 5643 | 987 | 07/27/2021 00:00:00 | 69862 |
| 7453 | 456 | 07/06/2021 00:00:00 | 69857 |
Answer:
This query first selects the distinct users who clicked on an ad and who added a product to the cart. Then, it joins these two sets of users. Finally, it calculates the click-through-rate and conversion rate.
To solve another question about calculating rates, try this TikTok SQL question within DataLemur's interactive SQL code editor:
How To Prepare for the Mu Sigma SQL Interview
The best way to prepare for a Mu Sigma SQL interview is to practice, practice, practice. In addition to solving the earlier Mu Sigma SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each SQL question has multiple hints, step-by-step solutions and best of all, there's an online SQL code editor so you can easily right in the browser your SQL query and have it checked.
To prep for the Mu Sigma SQL interview it is also useful to practice interview questions from other analytics & consulting companies like:
Explore the future of data-driven decision making with Mu Sigma!
However, if your SQL query skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers SQL concepts such as CASE/WHEN/ELSE statements and creating summary stats with GROUP BY – both of these pop up frequently during SQL interviews at Mu Sigma.
Mu Sigma Data Science Interview Tips
What Do Mu Sigma Data Science Interviews Cover?
In addition to SQL interview questions, the other question categories to prepare for the Mu Sigma Data Science Interview are:
- Statistics & Probability Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral Interview Questions centered on Mu Sigma values & principles
How To Prepare for Mu Sigma Data Science Interviews?
To prepare for Mu Sigma Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs
- a refresher on SQL, Product-Sense & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't ignore the behavioral interview – prep for that using this list of common Data Scientist behavioral interview questions.