9 Commvault SQL Interview Questions (Updated 2024)
At Commvault, SQL is used often for analyzing and managing large volumes of data backup and recovery, and for generating reports from structured and unstructured data. For this reason Commvault typically asks SQL query questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you practice for the Commvault SQL interview, this blog covers 9 Commvault SQL interview questions – how many can you solve?
9 Commvault SQL Interview Questions
SQL Question 1: Find the Most Valuable Customers of Commvault
Commvault is a company that specializes in data protection and information management software. They have a vast user base, of which, there are some customers who are more frequent and active in their operations. As a data analyst, your task is to identify these 'VIP' users, who are performing operations, such as backing up data, or managing databases very frequently in a month.
Let's assume that we have access to a 'user_operations' table that tracks the operations each user performs, including operation type, date, and amount of data processed. Here is a sample of how the table might look:
Example Input:
| operation_id | user_id | date | operation_type | data_processed (GB) |
|---|---|---|---|---|
| 8102 | 201 | 02/04/2022 | Backup | 70 |
| 7952 | 402 | 02/15/2022 | Manage Database | 120 |
| 6237 | 201 | 02/28/2022 | Backup | 80 |
| 6558 | 501 | 03/09/2022 | Backup | 150 |
| 8188 | 402 | 03/20/2022 | Manage Database | 120 |
Your task is to write a SQL query that identifies the top 10 users who processed the most GB of data in the past month.
Answer:
Here's a SQL query you might use, presuming that you want the top 10 power customers for the previous month:
This query groups operations by user, sums the amount of data processed for each user, and then sorts the users in descending order of total data processed. The clause is used to restrict the output to the top 10 users.
This will give you a list of top 10 user ids with most data processed in the last month, helping the company to identify and focus on their most active users.
To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: Analyze Average Purchase Price by Month for Different Products
Imagine you are a data analyst at Commvault. The company sells different backup software products. They want to analyze each product's average purchase price over each month, which will help them understand sales trends and make future decisions. They have a table tracking every sales transaction. The dataset contains fields such as , , , , and .
Based on the table, write a SQL query to compute the average purchase price by month for each product using window functions.
Example Input:
| transaction_id | user_id | transaction_date | product_id | purchase_price |
|---|---|---|---|---|
| 1 | 123 | 2022-03-01 00:00:00 | 50001 | 1200 |
| 2 | 124 | 2022-03-10 00:00:00 | 50002 | 1100 |
| 3 | 125 | 2022-04-05 00:00:00 | 50001 | 1320 |
| 4 | 126 | 2022-04-15 00:00:00 | 50002 | 1150 |
| 5 | 127 | 2022-05-20 00:00:00 | 50001 | 1300 |
Example Output:
| month | product | avg_purchase_price |
|---|---|---|
| 3 | 50001 | 1200.00 |
| 3 | 50002 | 1100.00 |
| 4 | 50001 | 1320.00 |
| 4 | 50002 | 1150.00 |
| 5 | 50001 | 1300.00 |
Answer:
In the above query, the function is used to get the month from the . Using the window function with clause, the average purchase price for each product is calculated for each month. This provides a breakdown of the average purchase price of each product by month.
To practice a related window function SQL problem on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
SQL Question 3: What are the different types of database indexes?
A database index is a data structure that improves the speed of data retrieval operations on a database table.
There are few different types of indexes that can be used in a database:
- Primary index: a unique identifier is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table.
- Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
- Clustered index: determines the physical order of the data in a table
For a concrete example, say you had a table of Commvault customer payments with the following columns:
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values. This speeds up queries that filter or sort the data based on the , as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of June, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
SQL Question 4: Average Data Usage per Client
As a data analyst at Commvault, a data protection and information management software company, you are asked to analyze the data usage of their client businesses. The management needs the information of average data usage by each client over the months, to understand the trend and provide better services.
To achieve this, assume that you have a table named which records each client's data usage details in GB's.
Example Input:
| client_id | usage_date | data_used_gb |
|---|---|---|
| 101 | 01/05/2022 | 30 |
| 102 | 01/05/2022 | 50 |
| 101 | 02/05/2022 | 35 |
| 102 | 02/05/2022 | 40 |
| 101 | 03/05/2022 | 25 |
| 102 | 03/05/2022 | 60 |
Answer:
PostgreSQL query to find the average data usage by each client over the months:
Example Output:
| client_id | month | avg_data_usage_gb |
|---|---|---|
| 101 | 5 | 30 |
| 102 | 5 | 50 |
This solution groups the data usage by client_id and month, and then calculates the average for each group. The result will give you the average data usage by each client for each month, which could be useful for understanding trends and patterns in data usage.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for requiring analytics on average, or this Facebook Active User Retention Question which is similar for involving client usage data.
SQL Question 5: Can you describe the difference between a correlated and a non-correlated sub-query?
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 Commvault 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 6: Average Monthly Sales Revenue by Product
In the context of Commvault, you might be asked to calculate the average revenue generated each month by each product.
Commvault is a data protection and information management software company, so you could imagine a table where lines represent sales transactions.
Consider the following table, .
Example Input:
| sale_id | sale_date | product_id | unit_price | qty |
|---|---|---|---|---|
| 1001 | 01/10/2022 | A101 | 600 | 5 |
| 1002 | 01/15/2022 | A101 | 600 | 3 |
| 1003 | 01/20/2022 | B202 | 400 | 1 |
| 1004 | 02/15/2022 | A101 | 600 | 4 |
| 1005 | 02/20/2022 | B202 | 450 | 2 |
Your task is to write a SQL query that returns a complete report containing: the month of sale, product id, and the average monthly sales revenue from each product. Sales revenue for a record is calculated as .
Example Output:
| month_of_sale | product | avg_revenue |
|---|---|---|
| 01 | A101 | 4200 |
| 01 | B202 | 400 |
| 02 | A101 | 2400 |
| 02 | B202 | 900 |
Answer:
In the answer, the TO_CHAR function is used to extract and format the month from the sale_date column. The AVG function is used in combination with the GROUP BY clause to calculate the average monthly revenue for each product. The calculation is used to calculate the revenue for each record in the table. The results are grouped by month and product, producing an average monthly revenue for each product.
SQL Question 7: What is a cross-join?
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.
Suppose you were building a Neural Network ML model, that tried to score the probability of a customer buying a Commvault product. Before you started working in Python and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Commvault products.
Here's a cross-join query you could use to find all the combos:
Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. For example, if you had 10,000 potential customers, and Commvault had 500 different product SKUs, you'd get 5 million rows as a result!!
SQL Question 8: Filtering Client Records
As a data analyst at Commvault, one of your tasks is to retrieve customer details from the database. The organization is particularly interested in customers from specific locations to target a marketing campaign.
You are asked to write a SQL query that retrieves customer information where the customer's address contains 'New York'. The fields that need to be retrieved include 'CustomerID', 'FirstName', 'LastName', 'Email' and 'Address'.
Example Input
| CustomerID | FirstName | LastName | Address | |
|---|---|---|---|---|
| 1001 | John | Doe | john.doe@test.com | 123 Street, New York, USA |
| 1002 | Jane | Smith | jane.smith@test.com | 456 Lane, New Jersey, USA |
| 1003 | Bruce | Wayne | bruce.wayne@test.com | 789 Drive, New York, USA |
| 1004 | Clark | Kent | clark.kent@test.com | 321 Avenue, California, USA |
| 1005 | Tony | Stark | tony.stark@test.com | 654 Boulevard, New York, USA |
Answer:
Example Output:
| CustomerID | FirstName | LastName | Address | |
|---|---|---|---|---|
| 1001 | John | Doe | john.doe@test.com | 123 Street, New York, USA |
| 1003 | Bruce | Wayne | bruce.wayne@test.com | 789 Drive, New York, USA |
| 1005 | Tony | Stark | tony.stark@test.com | 654 Boulevard, New York, USA |
In this SQL query, we used the keyword along with (wildcard character) to filter records containing 'New York' anywhere in the 'Address' column. This allows us to retrieve specific records from the database that match set criteria. The wildcard character serves as a placeholder for zero, one or multiple characters.
SQL Question 9: Calculating Product Sales Statistics
Imagine you work in the finance department of Commvault and you need to create a monthly report that displays the total sales and average sales per unit, rounded to two decimal places, of each product, as well as the root square of the total shipment weight for each product. Also, show the remainder when the total quantity sold is divided by 7 and the exponentiation result of total shipment weight raised to the power of 2.
Assume an "inventory" table exists, with the following layout:
Example Input:
| product_id | invoice_date | quantity_sold | unit_price | shipment_weight |
|---|---|---|---|---|
| 20001 | 01/02/2022 | 20 | 5.20 | 120.00 |
| 50002 | 01/14/2022 | 15 | 8.00 | 150.00 |
| 30001 | 02/08/2022 | 25 | 9.00 | 200.00 |
| 20001 | 02/20/2022 | 30 | 5.20 | 120.00 |
| 50002 | 03/14/2022 | 20 | 8.00 | 150.00 |
Answer:
This query calculates monthly statistics for each product, including the total sales (quantity_sold * unit_price), the average sales per unit (rounded to two decimal places), the root square of total shipment weight, the remainder when total quantity sold is divided by 7 and the exponentiation result of total shipment weight raised to the power of 2. Grouping is done by month and product_id, and the result is ordered by these fields.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring product sales calculations or this Amazon Average Review Ratings Question which is similar for requiring monthly reports and average calculations.
Commvault SQL Interview Tips
The best way to prepare for a Commvault SQL interview is to practice, practice, practice.
Beyond just solving the above Commvault SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each interview question has multiple hints, step-by-step solutions and most importantly, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it checked.
To prep for the Commvault SQL interview it is also wise to solve SQL problems from other tech companies like:
In case your SQL foundations are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers things like CTE vs. Subquery and CASE/WHEN statements – both of which show up often during SQL job interviews at Commvault.
Commvault Data Science Interview Tips
What Do Commvault Data Science Interviews Cover?
Besides SQL interview questions, the other types of questions covered in the Commvault Data Science Interview are:
- Stats Interview Questions
- Python Pandas or R Coding Questions
- Product-Sense Questions
- Machine Learning and Predictive Modeling Questions
- Resume-Based Behavioral Questions
How To Prepare for Commvault Data Science Interviews?
The best way to prepare for Commvault Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google, & Amazon
- A Crash Course covering SQL, Product-Sense & ML
- Great Reviews (900+ reviews, 4.5-star rating)