11 Varonis SQL Interview Questions (Updated 2025)

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.

Here are your sample tables:

activities Example Input:

users Example Input:

#### Answer:

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.

SELECT u.username, SUM(a.data_transferred) as total_data_transferred
FROM activities a
JOIN users u ON a.user_id = u.user_id
WHERE a.action_date > CURRENT_DATE - interval '30' day
GROUP BY u.user_id, u.username
ORDER BY total_data_transferred DESC
LIMIT 10;

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:

SQL Question 2: Analyze Access Event using Window Functions

Assume Varonis has a table called access_logs 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 access_logs table is as follows:

access_logs Example Input:

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:

Answer:

SELECT user_id,
       access_time AS last_access_time,
       file_id,
       file_name
FROM (
 SELECT user_id,
        access_time,
        file_id,
        file_name,
        ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY access_time DESC) as rn
 FROM access_logs
) t
WHERE t.rn = 1

The subquery in the FROM clause uses the ROW_NUMBER() function to assign a number to each row in the access_logs 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:

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.

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 customers table at your disposal with columns: customer_id, location, plan, is_active.

customers Example Input:

Answer:

SELECT *
FROM customers
WHERE location = 'United States'
AND plan IN ('Pro', 'Standard')
AND is_active = True;

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 WHERE and AND operators to apply these conditions.

Example Output:

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?

file_uses Example Input:

Example Output:

Answer:

SELECT 
    department, 
    AVG(file_size) as avg_file_size
FROM 
    file_uses
GROUP BY 
    department;

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 EXCEPT operator do?

The MINUS/EXCEPT 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 EXCEPT 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 EXCEPT 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 EXCEPT operator to find all contractors who never were a employee using this query:

SELECT first_name, last_name
FROM varonis_contractors

EXCEPT

SELECT first_name, last_name
FROM varonis_employees

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: ad_impressions that records every individual view on an ad, and ad_clicks that records every individual click on an ad.

ad_impressions Example Input:

ad_clicks Example Input:

Answer

SELECT
  impressions.product_id,
  (clicks.click_count::DECIMAL / impressions.impression_count) AS click_through_rate
FROM
  (
    SELECT
      product_id,
      COUNT(*) AS impression_count
    FROM
      ad_impressions
    WHERE
      DATE(impression_date) BETWEEN '2022-08-01' AND '2022-08-31'
    GROUP BY
      product_id
  ) AS impressions
JOIN
  (
    SELECT
      product_id,
      COUNT(*) AS click_count
    FROM
      ad_clicks
    WHERE
      DATE(click_date) BETWEEN '2022-08-01' AND '2022-08-31'
    GROUP BY
      product_id
  ) AS clicks
ON
  impressions.product_id = clicks.product_id

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:

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 Customers table and join it with the Sales table to get the result. For simplicity, assume each sale is associated with exactly one customer and that each sale has one item.

Customers Example Input:

Sales Example Input:

Answer:

SELECT c.first_name, c.last_name, SUM(s.amount_spent) AS total_spent
FROM Customers c
INNER JOIN Sales s 
ON c.customer_id = s.customer_id 
GROUP BY c.first_name, c.last_name;

Example Output:

This SQL query first joins the Customers table with the Sales table using an inner join on the customer_id column, which is common to both tables. The SUM aggregate function is then used to calculate the total amount_spent for each customer, with the result grouped by first_name and last_name. 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:

SQL Question 10: What does the CHECK 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:

CREATE TABLE ad_campaigns (
    ad_id INTEGER PRIMARY KEY,
    ad_name VARCHAR(128) NOT NULL,
    start_date DATE NOT NULL,
    end_date DATE NOT NULL,
    budget DECIMAL(12,2) NOT NULL CHECK (budget > 0),
    cost_per_click DECIMAL(12,2) NOT NULL CHECK (cost_per_click > 0)
);

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 sales transactions:

sales Example Input:

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:

Answer:

You can answer this question using the following PostgreSQL query:

SELECT   product_id,
         ROUND(SUM(revenue), 2)             AS total_revenue,
         ROUND(AVG(revenue), 2)             AS average_revenue,
         ROUND(STDDEV_POP(revenue), 2)      AS std_deviation_revenue
FROM     sales
GROUP BY product_id
ORDER BY product_id;

This query groups the sales by product and calculates the total revenue (SUM), the average revenue (AVG), and the population standard deviation of the revenue (STDDEV_POP). It then rounds these quantities to two decimal places using ROUND. 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.

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:

• CyberArk SQL questions

• Okta SQL questions

• Zscaler SQL questions

• Ping Identity SQL questions

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.

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:

• Statistics and AB Testing Questions
• Python or R Coding Questions
• Analytics and Product-Metrics Questions
• ML Modelling Questions
• Behavioral & Resume-Based Questions

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)