10 CyberArk SQL Interview Questions (Updated 2024)
At CyberArk, SQL is used across the company for analyzing application performance data and managing cybersecurity threat databases. That's why CyberArk almost always evaluates jobseekers on SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you ace the CyberArk SQL interview, this blog covers 10 CyberArk SQL interview questions – can you solve them?
10 CyberArk SQL Interview Questions
SQL Question 1: Identify The Power Users in CyberArk
Given the context of CyberArk, a global leader in privileged access management, a major source of enterprise security. The power users or VIP users would be those users who frequently access the privileged access services.
This activity is immensely important for CyberArk as these types of users tend to highlight key operational aspects of privileged access management that require the most attention and resource allocation in regards to monitoring, management and innovation.
Let's assume for CyberArk, we have a table named containing records of each user accessing privileged access services. Each time a user accesses a service, a record is generated all stored in a database. The table contains the , and .
Now, for this problem, we consider VIP users as those who have accessed the services for more than 100 times in the last month.
Example Input:
| user_id | access_date | service_id |
|---|---|---|
| 001 | 2022-08-02 | 1001 |
| 001 | 2022-08-03 | 1010 |
| 002 | 2022-08-01 | 1001 |
| 002 | 2022-08-05 | 1011 |
| 001 | 2022-08-09 | 1002 |
| ... | ... | ... |
Your task is to write a SQL query to identify the VIP users for CyberArk in the last month.
Answer:
The following PostgreSQL query will suffice:
This query works by filtering the table for entries where the is within the last month. It then groups these entries by , and counts the number of records for each - this gives us the number of times each user has accessed the privileged access service in the last month. The clause is then used to filter these results for users who have accessed the service more than 100 times, giving us our VIP users.
Example Output:
| user_id |
|---|
| 007 |
| 012 |
| 399 |
| ... |
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: Calculate Cumulative Login Count by User
Given an events table that logs users' login activities to CyberArk software, write a query to calculate a cumulative count of logins for each user, ordered by the event timestamp.
Assume the structure of the database table is as follows:
Example Input:
| event_id | user_id | event_type | event_timestamp |
|---|---|---|---|
| 101 | 35 | login | 2021-01-01 00:00:00 |
| 102 | 42 | login | 2021-01-02 00:00:00 |
| 103 | 35 | logout | 2021-01-02 00:05:00 |
| 104 | 35 | login | 2021-01-02 00:10:00 |
| 105 | 42 | login | 2021-01-03 00:10:00 |
We only care about event types.
Example Output:
| user_id | event_timestamp | cumulative_login_count |
|---|---|---|
| 35 | 2021-01-01 00:00:00 | 1 |
| 42 | 2021-01-02 00:00:00 | 1 |
| 35 | 2021-01-02 00:10:00 | 2 |
| 42 | 2021-01-03 00:10:00 | 2 |
Answer:
The query uses a PostgreSQL window function to calculate the cumulative login count. Specifically, sets the window frame to all preceding rows of the current row in the order defined by , inclusive of the current row. Only the rows with 'login' as are counted.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's interactive SQL code editor:
SQL Question 3: When would you use a clustered index vs. a non-clustered index?
Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 4: CyberArk User Activity Monitoring
CyberArk is a company that offers services to protect enterprise companies from cyber threats. One of their many services is User Activity Monitoring which records, audits, and monitors all user activity. Let's suppose you are tasked to design a database to record every employee's activities and retrieve the data when required for auditing.
Consider the following tables: , and .
| user_id | name | role |
|---|---|---|
| 1 | John Doe | Admin |
| 2 | Jane Smith | User |
| 3 | Mark Johnson | Editor |
| activity_id | activity_name |
|---|---|
| 1 | Login |
| 2 | Logout |
| 3 | Change Password |
| 4 | Update Profile |
| log_id | user_id | activity_id | date_time |
|---|---|---|---|
| 1 | 1 | 1 | 06/10/2022 09:00:00 |
| 2 | 1 | 4 | 06/10/2022 09:30:00 |
| 3 | 2 | 1 | 06/10/2022 10:00:00 |
| 4 | 2 | 2 | 06/10/2022 12:00:00 |
| 5 | 3 | 1 | 06/10/2022 11:00:00 |
Your task is to write an SQL PostgreSQL query to retrieve audit information of actions .The information should include , , and ordered by .
Answer:
This PostgreSQL SQL query will return a result where each row represents an individual action performed by a ordered by . The result includes the detail about the who performed the action ( and ), the that was performed, and the when it was performed.
SQL Question 5: What are the similarities and difference between relational and NoSQL databases?
While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways, most importantly on the way data is stored. Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.
This added flexibilty makes NoSQL databases great for non-tabular data (like hierarchal data or JSON data), or data where the type/format is constantly evolving. With this added flexibility, comes one big weakness – you won't get ACID-compliance. That means, unlike relational databases which are typically adhere to the ACID properties (atomic, consistent, isolated, and durable), you don't get as strong guarentees with most non-relational databases.
SQL Question 6: Find Average Incident Severity
As a data analyst at CyberArk, a cybersecurity company, you are given a task to find the average severity of security incidents detected by their software, for each client, on a monthly basis.
Example Input:
| incident_id | client_id | detect_date | severity |
|---|---|---|---|
| 1010 | 200 | 06/08/2022 00:00:00 | 3 |
| 1020 | 205 | 06/18/2022 00:00:00 | 5 |
| 1030 | 200 | 06/12/2022 00:00:00 | 3 |
| 1040 | 220 | 07/26/2022 00:00:00 | 4 |
| 1050 | 220 | 07/05/2022 00:00:00 | 2 |
Answer:
Example Output:
| client_id | month | avg_severity |
|---|---|---|
| 200 | 6 | 3.00 |
| 205 | 6 | 5.00 |
| 220 | 7 | 3.00 |
This query groups the data by and of . It then takes the average of the for each group to get the average severity of incidents for each client on a monthly basis. This could help CyberArk to track and compare the incident severity among different clients or to monitor the trend for a particular client.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages for categories or this Lyft Active User Retention Question which is similar for data analysis on a monthly basis.
SQL Question 7: How would you improve the performance of a slow SQL query?
Here's some strategies that can generally speed up a slow SQL query:
- Only query the columns you actually need
- Index the columns used in the query
- Use the right kind of JOIN (be explicit about inner vs. outer joins)
- Use the right data types (for example INT vs BIGINT can save you disk space and memory)
While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at CyberArk, knowing the above basics should be more than enough to ace the SQL interview! Data Engineers on the other hand should know more about the command and understand a bit more about a query-execution plan.
SQL Question 8: Calculate the Click-through Conversion Rate for CyberArk's Digital Products
Let's imagine CyberArk, a global digital company, wants to understand the conversion of their new software product from the action of viewing the product's page to adding the product to the cart. They are particularly interested in testing the clickthrough conversion rate in the first week after the software launch.
To that end, your task is to write a PostgreSQL query that will calculate the click-through conversion rate (CTR) for their new software product in the first week, with CTR defined as the number of 'add to cart' actions divided by the number of 'view product' actions.
Example Input:
| log_id | user_id | log_time | action |
|---|---|---|---|
| 9051 | 1123 | 10/08/2022 09:00:00 | view product |
| 2404 | 2659 | 10/08/2022 09:05:00 | view product |
| 7053 | 3652 | 10/08/2022 09:10:00 | add to cart |
| 9866 | 1912 | 10/08/2022 09:15:00 | view product |
| 4067 | 9821 | 10/08/2022 09:20:00 | add to cart |
Answer:
This query calculates the click-through conversion rate by first aggregating the number of 'add to cart' actions and 'view product' actions in the first week after the launch into separate Common Table Expressions (CTEs). Then, we divide the count of 'add to cart' by the count of 'view product' actions to obtain the CTR. Note that when performing this division, we convert the count of 'add to cart' actions to a decimal to ensure accurate division results (rather than integer division).
To practice a similar SQL problem on DataLemur's free online SQL coding environment, try this Meta SQL interview question:
SQL Question 9: Calculate bonus for each sales person based on total sales
For this exercise, suppose you are working as a Data Analyst at CyberArk and tasked with calculating bonuses for sales people. The bonus is calculated based on the total value of products each sales person sells. The bonus calculation is as follows:
- If the total sales is under 10,000, the bonus is 2% of the total sales
- If the total sales is between 10,000 and 50,000, the bonus is 3% of the total sales
- If the total sales is above 50,000, the bonus is 5% of the total sales
Each product has a fixed price and sales information is stored in a table and product information is stored in a table. Information regarding sales person is stored in table.
Write a SQL query to calculate the bonus for each sales person. Round off the bonus value to nearest integer using ROUND function.
Example Input:
| sale_id | product_id | sales_person_id |
|---|---|---|
| 10 | 100 | 1 |
| 20 | 200 | 2 |
| 30 | 100 | 2 |
| 40 | 300 | 1 |
| 50 | 200 | 3 |
Example Input:
| product_id | price |
|---|---|
| 100 | 500 |
| 200 | 1000 |
| 300 | 2000 |
Example Input:
| sales_person_id | name |
|---|---|
| 1 | Alice |
| 2 | Bob |
| 3 | Charlie |
Example Output:
| sales_person_name | bonus |
|---|---|
| Alice | 50 |
| Bob | 90 |
| Charlie | 30 |
Answer:
The query first joins the sales, products, and sales_persons tables based on product_id and sales_person_id. It then calculates the sum of price for each sales person. Depending upon the total sales value, it applies the bonus condition using a CASE statement and rounds the bonus using the ROUND function. The bonus and sales person's name are then selected for the final output.
An index in a database is a data structure that helps to quickly find and access specific records in a table.
For example, if you had a database of CyberArk customers, you could create a primary index on the column.
Having a primary index on the column can speed up performance in several ways. For example, if you want to retrieve a specific customer record based on their , the database can use the primary index to quickly locate and retrieve the desired record. The primary index acts like a map, allowing the database to quickly find the location of the desired record without having to search through the entire table.
Additionally, a primary index can also be used to enforce the uniqueness of the column, ensuring that no duplicate values are inserted into the table. This can help to prevent errors and maintain the integrity of the data in the table.
CyberArk SQL Interview Tips
The key to acing a CyberArk SQL interview is to practice, practice, and then practice some more!
Beyond just solving the above CyberArk SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each interview question has multiple hints, full answers and best of all, there's an interactive SQL code editor so you can instantly run your SQL query answer and have it executed.
To prep for the CyberArk SQL interview you can also be helpful to practice interview questions from other tech companies like:
But if your SQL coding skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers topics including LEFT vs. RIGHT JOIN and filtering data with boolean operators – both of which come up frequently during CyberArk interviews.
CyberArk Data Science Interview Tips
What Do CyberArk Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions to prepare for the CyberArk Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for CyberArk Data Science Interviews?
The best way to prepare for CyberArk Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
- A Refresher covering Stats, ML, & Data Case Studies
- Great Reviews (900+ 5-star reviews on Amazon)