11 Varonis SQL Interview Questions (Updated 2024)

Updated on

March 2, 2024

At Varonis, SQL is used all the damn time for extracting and for helping their cusotomers discover database vulnerabilities. Unsurprisingly this is why Varonis often tests SQL coding questions during interviews for Data Science and Data Engineering positions.

As such, to help you ace the Varonis SQL interview, we've collected 11 Varonis SQL interview questions – able to answer them all?

11 Varonis SQL Interview Questions

SQL Question 1: Identify the Top Performing Users in Term of Data Usage

In Varonis company, users use the company's services to protect and manage their data. For this operation, an activity log is maintained in the database recording each user's data transfer activity. Your task is to write a SQL query to identify the top 10 users who have transferred the most data in the last 30 days. Assume that "data_transferred" is in GB.


Example Input:
activity_iduser_idaction_datedata_transferred
348578109/05/2022 00:00:002
356796209/06/2022 00:00:005
349078109/13/2022 00:00:003
357496209/17/2022 00:00:001
358198109/15/2022 00:00:0010
357578109/20/2022 00:00:002
Example Input:
user_idusernamelast_activity_date
781nick_chalk09/20/2022 00:00:00
962mat_saunders09/17/2022 00:00:00
981bob_morton09/15/2022 00:00:00

You can solve this problem by summing up the data transferred by each user in the last 30 days and ordering the result in descending order. Restricting the row count to 10 will give you the top 10 users.


This query works by joining the activities table with the users table on the user_id field. We then filter out only the activities for the last 30 days. For each user, we sum the amount of data transferred and order the total data transferred in descending order. The LIMIT clause is used to get only the top 10 users.

To practice a super-customer analysis question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question: Microsoft SQL Interview Question: Super Cloud Customer

SQL Question 2: Analyze Access Event using Window Functions

Assume Varonis has a table called to keep track of all file access events. Each row in the table represents a file access event, which includes information about the user, the file that was accessed, and the timestamp of the access event.

The structure of the table is as follows:

Example Input:
log_iduser_idaccess_timefile_idfile_name
102433412022-08-01 09:32:002356confidential.docx
102447212022-08-05 10:19:004533sales_report.xlsx
102453412022-08-05 14:00:002356confidential.docx
102461232022-08-06 13:32:006742project_plan.pdf
102474542022-08-10 17:00:002356confidential.docx

The task here is to write a PostgreSQL query that returns the most recent file access event for each user.

Outcome should be as follows:

Expected Output:

user_idlast_access_timefile_idfile_name
3412022-08-05 14:00:002356confidential.docx
7212022-08-05 10:19:004533sales_report.xlsx
1232022-08-06 13:32:006742project_plan.pdf
4542022-08-10 17:00:002356confidential.docx

Answer:


The subquery in the FROM clause uses the ROW_NUMBER() function to assign a number to each row in the table where the rows are partitioned by user_id and ordered by the access_time in descending order. The outer query then selects only the rows where the row number is 1, which are the most recent file access event for each user.

To solve a related window function SQL problem on DataLemur's free interactive SQL code editor, solve this Amazon SQL question asked in a BI Engineer interview: Amazon Highest-Grossing Items SQL Analyis Question

SQL Question 3: Can you describe the difference between a clustered and a 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.

Varonis SQL Interview Questions

SQL Question 4: Filtering Customer Data Based on Locations and Plans

You have been given the task to help Varonis analyze their customer data. Varonis is interested in understanding the customer distribution by locations and various subscription plans. The objective is to isolate records of customers who reside in United States, use a 'Pro' or 'Standard' plan, and have their plan active.

You need to write a SQL query that filters out these customers from the existing customers table. You have the table at your disposal with columns: , , , .

Example Input:
customer_idlocationplanis_active
101United StatesProTrue
102CanadaFreeTrue
103United StatesFreeFalse
104United StatesStandardTrue
105United KingdomProTrue
106United StatesProFalse

Answer:


This query filters out the customers based on the location set as 'United States', the plan as either 'Pro' or 'Standard', and the active status of the plan set as 'True'. It uses the and operators to apply these conditions.

Example Output:
customer_idlocationplanis_active
101United StatesProTrue
104United StatesStandardTrue

SQL Question 5: Can you explain the distinction between cross join and natural join?

Cross join and natural join are like two sides of a coin in the world of SQL.

Cross joins is like the wild and reckless cousin who creates a giant new table by combining every row from table A with every row from table B, no questions asked, no common key needed.

Natural joins are like the more refined and selective cousin who only combines rows from multiple tables if they have something in common (i.e., common columns/keys).

While cross join doesn't discriminate and will create a massive table if given the chance, natural join is more selective and only returns a table with the number of rows equal to the number of matching rows in the input tables. So, choose your JOIN wisely!

SQL Question 6: Find the Average File Sizes in Each Department's Drive

As a Data Scientist at Varonis, a company that specializes in data security and analytics software, you are tasked with the job of analyzing data usage within the company. Given a database of files with their sizes and the departments that use them, can you write a SQL query to find the average file size of each department's drive?

Example Input:
file_idfile_size(MB)department
1001125.5Sales
1002250.7Sales
100345.2HR
1004317.9Marketing
100578.6HR
1006152.2Marketing
Example Output:
departmentavg_file_size(MB)
Sales188.1
HR61.9
Marketing235.05

Answer:


This query works by grouping the data by the department and then using the AVG() function to calculate the average file size for each department. The resulting output gives the average file size for each department's drive.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for grouping data by a specified field and calculating a value related to it or this Facebook Average Post Hiatus (Part 1) Question which is similar for its use in calculating an average of a column.

SQL Question 7: What does the operator do?

The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since Varonis interviewers aren't trying to trip you up on memorizing SQL syntax).

For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for Varonis, and had access to Varonis's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.

You could use operator to find all contractors who never were a employee using this query:


SQL Question 8: Calculate the Click-Through Rate for Product Ads

At Varonis, we use online marketing to promote several of our products. In this scenario, we want to calculate the click-through rate (CTR) for each product ad for the month of August 2022. Click-through rate is the ratio of users who click on a specific link to the number of total users who view a page, email, or ad. We have two tables: that records every individual view on an ad, and that records every individual click on an ad.

Example Input:
impression_idproduct_iduser_idimpression_date
1015000112308/05/2022 00:00:00
1026985226508/05/2022 00:00:00
1035000136208/12/2022 00:00:00
1044000119208/15/2022 00:00:00
1056985298108/20/2022 00:00:00
Example Input:
click_idproduct_iduser_idclick_date
2015000112308/05/2022 00:00:00
2026985226508/15/2022 00:00:00
2034000119208/15/2022 00:00:00
2045000136208/20/2022 00:00:00
2056985298108/20/2022 00:00:00

Answer


In this query, we are first grouping the impressions and clicks on product and count them respectively. Then we join these counts together by product_id and calculate the click-through rate, which is the total number of clicks divided by the total number of impressions. These are both filtered for the date range of August 2022. The final result provides the CTR for each product ad for August 2022.

To practice a similar problem about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive SQL code editor: Signup Activation Rate SQL Question

SQL Question 9: Joining Customer and Sales Tables for Insight

As a data analyst at Varonis, your manager asks you to analyze the data of customers and their respective purchases. Specifically, you are tasked with writing a query to find the total amount spent by each customer. Use the table and join it with the table to get the result. For simplicity, assume each sale is associated with exactly one customer and that each sale has one item.

Example Input:
customer_idfirst_namelast_name
1JohnDoe
2MaryJohnson
3JamesSmith
Example Input:
sale_idcustomer_idamount_spent
1011200
1022150
1033300
1041150
1052275

Answer:


Example Output:
first_namelast_nametotal_spent
JohnDoe350
MaryJohnson425
JamesSmith300

This SQL query first joins the table with the table using an inner join on the column, which is common to both tables. The aggregate function is then used to calculate the total for each customer, with the result grouped by and . The result is a list of customers and the corresponding total amount they've spent.

Because join questions come up frequently during SQL interviews, try this Snapchat JOIN SQL interview question: Snapchat Join SQL question

SQL Question 10: What does the constraint do, and when might you use it?

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.

For example, say you had a marketing analytics database that stores ad campaign data from Varonis's Google Analytics account.

Here's what some constraints could look like:


The CHECK constraint is used in the above example to make sure that the "budget" and "cost_per_click" fields contain only positive values. This helps to maintain the validity and integrity of the data in the context of ad campaigns.

The CHECK constraint can also be used to enforce other specific conditions on data. For example, you can use it to ensure that the "start_date" for each ad campaign is earlier than the "end_date."

SQL Question 11: Calculating Aggregate Financial Metrics

Varonis is interested in getting more insights into their financial performance. They specifically want to aggregate their revenue data by product in a way that allows them to see the total revenue, average revenue, and the standard deviation of the revenue for each product.

Consider the following simple data table of transactions:

Example Input:
sale_idproduct_idrevenuesale_date
18921001100.002021-07-31
18931001150.002021-08-01
18941002200.002021-08-01
18951002300.002021-08-02
18961002400.002021-08-03

The expected output should display the product id, total revenue generated by each product, the average revenue per sale, and the standard deviation of the revenue.

Example Output:
product_idtotal_revenueaverage_revenuestd_deviation_revenue
1001250.00125.0035.36
1002900.00300.00100.00

Answer:

You can answer this question using the following PostgreSQL query:


This query groups the sales by product and calculates the total revenue (), the average revenue (), and the population standard deviation of the revenue (). It then rounds these quantities to two decimal places using . Note that SQL might give a slightly different result for the standard deviation due to its method of computation.

Preparing For The Varonis SQL Interview

The key to acing a Varonis SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier Varonis SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Netflix, Google, and Amazon. DataLemur Question Bank

Each problem on DataLemur has multiple hints, step-by-step solutions and best of all, there is an online SQL code editor so you can instantly run your query and have it checked.

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

In case your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL interview tutorial.

SQL tutorial for Data Scientists & Analysts

This tutorial covers topics including Self-Joins and WHERE with AND/OR/NOT – both of which show up frequently in Varonis SQL assessments.

Varonis Data Science Interview Tips

What Do Varonis Data Science Interviews Cover?

Beyond writing SQL queries, the other topics to practice for the Varonis Data Science Interview are:

Varonis Data Scientist

How To Prepare for Varonis Data Science Interviews?

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

  • 201 Interview Questions from Facebook, Google, & Amazon
  • A Refresher covering SQL, Product-Sense & ML
  • Amazing Reviews (900+ 5-star reviews on Amazon)

Ace the Data Science Interview Book on Amazon

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