9 Nutanix SQL Interview Questions (Updated 2024)

Updated on

March 1, 2024

At Nutanix, SQL is used quite frequently for extracting raw data from their product usage databases, and for generating insights into their cloud infrastructure management business. They also sell a service to help you virtualize Microsoft SQL Server databases. Because of this, Nutanix frequently asks SQL problems in interviews for Data Science and Data Engineering positions.

To help you study for the Nutanix SQL interview, we've curated 9 Nutanix SQL interview questions – can you solve them?

9 Nutanix SQL Interview Questions

SQL Question 1: Identifying Nutanix Power Users

Nutanix is a technology company specializing in cloud services and software-defined storage. For their business, power users could be identified as users who are utilizing Nutanix's resources heavily and often. This can be reflected in the users who continuously create a high number of virtual machines (VMs) over a short period, say 6 months.

Assuming we have the following database tables:


11310/20/2017 00:00:00john@company.com
43203/15/2018 00:00:00mary@product.com
54806/30/2019 00:00:00sam@example.com


851210/15/2021 00:00:00113
193210/20/2021 00:00:00113
342511/05/2021 00:00:00432
947104/24/2022 00:00:00548
383204/25/2022 00:00:00548
783204/30/2022 00:00:00548

Write a SQL query to determine the top 10 users who have created the most VMs over the last 6 months.


This SQL query first forms a CTE (Common Table Expression) , which counts the VMs each user has created over the past 6 months. It then joins this CTE with the table on to get the user's email. The query finally orders the results by the number of VMs in descending order and limits the output to the top 10 users.

To practice a similar customer analytics SQL question where you can solve it interactively and have your SQL query instantly executed, try this Walmart Labs SQL Interview Question: Walmart SQL Interview Question

SQL Question 2: Compute the Migration Count of Nutanix VMs

You are provided a dataset in which we track each time a virtual machine (VM) is migrated from one host to another in Nutanix clusters over a given period. You need to write a SQL query to find out how many times each VM migrated based on monthly data.

A VM migration means when the host_id of the VM changes. Assume a VM cannot migrate more than once per day, but can migrate between different hosts on different days.

The following are the columns in the table:

  • : An identification number for each migration
  • : A unique identification number for each VM
  • : The date of migration
  • : A unique identification number for the host to which the VM was migrated on that day
Example Input:

The output should return the , , and sorted in ascending order by and then .

Example Output:


The above query takes each record in the table and partitions it by and the month of . The COUNT() function then counts the number of records, which represents the number of migrations for each VM on a monthly basis. The result is then ordered by and month of the migration.

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

DataLemur Window Function SQL Questions

SQL Question 3: What are the ACID properties in a DBMS?

A DBMS (database management system), in order to ensure transactions are relaible and correct, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability

Here is what each of the ACID properties stands for:

Atomicity: ensures that a transaction is either completed in its entirety, or not completed at all. If a transaction fails halfway, the database does a rollback on the commit.

Consistency: ensures that a transaction will only be completed if adheres to the constraints defined in the database of the DB.

Isolation: ensures that concurrent transactions are isolated from each one another, so that the changes made by one transaction cannot be seen by a 2nd transaction until the 1st transaction is done.

**Durability: ** ensures that once a transaction has been committed, the database permanently stores the results in the DB.

As you can see, it's pretty important for Nutanix's data systems to be ACID compliant, else they'll be a big problem for their customers!

Nutanix SQL Interview Questions

SQL Question 4: Fetching Nutanix Customers based on Certain Conditions

As a data scientist at Nutanix, you are tasked with examining the database of customers who have initiated support cases. From the and tables, find the customers who have initiated more than 3 priority 1 cases (priority ranges from 1-3, priority 1 being the highest) within the last year. From the obtained list, further shortlist the customers who are from the 'IT' industry for a priority revision consulting. Here are the schemas of these tables.

Example Input:
1001Company X02/10/2018Finance
2002Company Y03/05/2019IT
3003Company Z07/01/2020IT
4004Company A01/01/2021Healthcare
5005Company B06/12/2021IT
Example Input:


This query first filters the list of cases to include only those that have been initiated within the last year and have a priority level of 1. It then counts the number of these cases for each customer in the table. The resulting table is joined on with the table to add the information. Finally, we filter the combined table to include only those rows where the number of priority 1 cases exceeds 3 and the customer's industry is 'IT'.

SQL Question 5: What is database denormalization, and when is it a good idea to consider it?

Database denormalization is when you add redundancy to a database, and break typical normalization rules (specified by 1st, 2nd, 3rd normal forms). There's 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, as joins can be expensive and slow.

  • Simplification: Denormalization can also be used to simplify the design of a database by reducing the number of tables and relationships that need to be managed. This can make it easier to understand and maintain the database.

  • 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.

While denormalization can be a useful tool for improving performance and scalability, it's important to keep in mind that it can make update and delete operations more complex. This is because denormalization can create data duplicates, which can make it harder to maintain data integrity. So, to avoid any headaches, it's a good idea to start with a well-normalized database design and then consider denormalization only if it's absolutely necessary for your specific performance and scalability needs. In other words, denormalization can be a bit of a wild card, so it's best to handle it with care!

SQL Question 6: Average Purchased Price of Products per Customer

Assume Nutanix has a customer database that stores customer details and a transaction database that records the details of every transaction that a customer has made. Write a SQL query to calculate the average purchased price of each product per customer. Join the two tables using the customer_id.

Example Input:
Example Input:


This question tests your knowledge of joining multiple tables and using aggregate functions in SQL. The query first joins the "customer" and "transactions" tables using the customer_id field which is common in both tables. It then groups the result by the customer (their id, first and last name) and product_id to calculate the average purchased price of each product per customer.

Since joins come up frequently during SQL interviews, try this interactive Snapchat JOIN SQL interview question: Snapchat JOIN SQL interview question

SQL Question 7: What are the similarities and differences between correleated and non-correlated sub-queries?

A correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.

Here is an example of a correlated sub-query:

This query selects the and total sales of all Nutanix customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().

Here is an example of a non-correlated sub-query:

The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.

Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.

SQL Question 8: Calculate Statistics from Server Resource Usage

Nutanix is a leading company providing cloud software and hyper-converged infrastructure solutions. One of their primary metrics for performance monitoring is the usage of resources on their servers.

Consider two tables which stores the metadata of servers maintained by Nutanix and which records resource usage statistics for these servers over time. Your task is to write a SQL query to calculate the average CPU usage and maximum memory usage of each server in the month of September 2022 and round the result to 2 decimal points. Additionally, calculate the modulus of difference between average CPU and max memory by 7 and find its square root.

Example Input:
101'Server_A'01/02/2020 00:00:00
102'Server_B'04/08/2021 00:00:00
103'Server_C'11/20/2019 00:00:00
Example Input:
10109/15/2022 04:00:00658
10109/17/2022 08:00:00709
10209/01/2022 12:00:00304
10209/04/2022 16:00:00296
10309/07/2022 19:00:0010011
Example Output:


This query first joins the and tables on the column and filters out the records for the month of September 2022. It then groups the results by and calculates the required metrics. The averaging of CPU usage (avg_cpu_usage) and maximum memory usage (max_memory_usage) are straightforward, but the 'final_calculation' includes additional processing, using the function to find the modulus of the absolute difference between the average CPU usage and max memory usage by 7, and then the function to find its square root. The function is used throughout to limit the precision to 2 decimal places.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for handling server utilization data or this Microsoft Supercloud Customer Question which is similar for querying with company's server data.

SQL Question 9: Monthly Active Users on Nutanix Cloud Services

Nutanix is a cloud computing software company that develops and offers a wide range of software-defined storage products and services. Among these services, the tracking and analysis of user engagement are instrumental for company growth.

For this exercise, never assume that you have a 'users' table and an 'activity_log' table. The 'users' table consists of user details with fields 'user_id', 'user_name', and 'country'. The 'activity_log' table logs user activity with fields 'activity_id', 'user_id', 'activity_type', and 'timestamp'.

The task at hand is to write a SQL query to determine the count of monthly active users (MAUs) for each country. An active user is defined as a user who has performed any activity in a given month.

Example Input:
Example Input:
500011001Login2022-06-08 08:00:00
500021001Logout2022-06-08 16:00:00
500031002Login2022-06-10 08:00:00
500041003Login2022-06-12 09:00:00
500051002Logout2022-06-10 16:00:00
500061001Login2022-07-01 08:00:00
500071004Login2022-07-03 08:00:00


This PostgreSQL query will join the 'users' table and the 'activity_log' table on 'user_id'. It then groups the data by both month (extracted from timestamp) and country, and makes use of the COUNT DISTINCT function to count the unique user_ids (representing active users) within each group. The ORDER BY statement is used to arrange the output by month and country for easier interpretation.

Nutanix SQL Interview Tips

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

Each interview question has hints to guide you, full answers and crucially, there is an online SQL code editor so you can instantly run your SQL query and have it executed.

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

However, if your SQL coding skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this SQL interview tutorial.

SQL tutorial for Data Analytics

This tutorial covers SQL topics like finding NULLs and filtering groups with HAVING – both of these show up routinely during Nutanix SQL assessments.

Nutanix Data Science Interview Tips

What Do Nutanix Data Science Interviews Cover?

Besides SQL interview questions, the other topics to practice for the Nutanix Data Science Interview are:

  • Stats Interview Questions
  • Coding Questions in Python or R
  • Open-Ended Data Case Studies
  • ML Interview Questions
  • Behavioral Interview Questions

Nutanix Data Scientist

How To Prepare for Nutanix Data Science Interviews?

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

  • 201 Interview Questions from Microsoft, Amazon & startups
  • A Refresher on Product Analytics, SQL & ML
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