9 SentinelOne SQL Interview Questions (Updated 2024)
At SentinelOne, SQL is used across the company for analyzing cybersecurity threat data and building data models for advanced threat prediction and detection. Because of this, SentinelOne typically asks SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you study for the SentinelOne SQL interview, we'll cover 9 SentinelOne SQL interview questions – able to solve them?
9 SentinelOne SQL Interview Questions
SQL Question 1: Calculate the average vulnerability score per device
SentinelOne, being a cybersecurity company, constantly encounters threats. To maintain a robust defense system, it is vital to monitor the overall vulnerability of each device protected by SentinelOne's services. Consider you have access to a dataset that records threat logs per device. Each log entry has a (ranging from 1 to 10, with 10 being the highest level of vulnerability), , and a .
Write a query to calculate the average vulnerability score for each device, per week, using a rolling average with a window of the previous 3 weeks (including the current week). Assume that weeks start on Mondays and are identified by the year and week number of the year (YYYYWW format).
Example Input:
| log_id | device_id | timestamp | vulnerability_score |
|---|---|---|---|
| 1 | 100 | 2022-07-07 | 7 |
| 2 | 100 | 2022-07-14 | 6 |
| 3 | 100 | 2022-07-24 | 8 |
| 4 | 100 | 2022-07-31 | 9 |
| 5 | 101 | 2022-07-07 | 4 |
| 6 | 101 | 2022-07-15 | 2 |
| 7 | 101 | 2022-07-20 | 3 |
| 8 | 101 | 2022-07-31 | 5 |
Answer:
The query would look something like this:
In this query, we first group the data by and (using function to convert the date to the start of the week). Then, we calculate the rolling average vulnerability score for each device within a window of 3 weeks (the current week and the two preceding weeks), employing the window function. This captures the average score for each week, giving us an overview of each device's vulnerability status.
For more window function practice, try this Uber SQL problem on DataLemur's interactive SQL code editor:
SQL Question 2: SentinelOne Database Design
SentinelOne is a cybersecurity company that specializes in autonomous endpoint protection, which helps to prevent and detect cyber threats. One of the key data that SentinelOne deals with is threat details detected on customer endpoints.
Assume that you are tasked to design a database that holds information about the company's customers, the endpoints, and the threats detected. The three main tables would be Customers, Endpoints and Threats, with the relationships being that a Customer has many Endpoints and an Endpoint can have multiple Threats.
The "Customers" table should include the customer_id, name, contact, and subscription level (Free, Basic, Premium).
The "Endpoints" table will correspond to the different devices belonging to the customer and should include endpoint_id, customer_id, operating system (such as windows, mac, etc.), and endpoint status (Active, Inactive).
The "Threats" table will store the details of the threats detected on the endpoints. This should include threat_id, endpoint_id, detection time, threat level (High, Medium, Low), and threat description.
Provide the structures of three tables as follows:
Example Input:
| customer_id | name | contact | subscription_level |
|---|---|---|---|
| 1 | SentinelCorp | sentinelcorp@email.com | Premium |
| 2 | SafeSky | safesky@email.com | Basic |
| 3 | SecureSea | securesea@email.com | Free |
Example Input:
| endpoint_id | customer_id | operating_system | endpoint_status |
|---|---|---|---|
| 1 | 1 | Windows | Active |
| 2 | 1 | Mac | Inactive |
| 3 | 2 | Windows | Active |
| 4 | 3 | Android | Active |
Example Input:
|threat_id|endpoint_id|detection_time|threat_level|threat_description| |----|----|----|----| |1|1|2022-06-05 10:22:00|High|Malware Detected| |2|1|2022-06-05 12:05:00|Medium|Suspicious Activity Detected| |3|2|2022-06-08 17:06:00|High|Ransomware Detected| |4|4|2022-06-10 19:20:00|Low|Potential Phishing Attempted|
Answer:
Using the PostgreSQL syntax, the SQL command to join the three tables to present the customer name, endpoint details, and related threat detected would be:
This query joins the 'Customers', 'Endpoints', and 'Threats' tables using the respective ID fields. It outputs a table showing details about the customer, their endpoint, and the detected threat for each record, providing valuable insights into the threats detected and their related information. This could aid SentinelOne in identifying patterns and enhancing their cybersecurity measures.
SQL Question 3: How would you speed up a slow SQL query?
There's several steps you can take to troubleshoot a slow SQL query.
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. After that, you can start changing your query, depending on what the source of the query performance issue is.
Generally, indexes can help speed up queries. Also de-normalizing your tables might help, to remove slow joins.
Lastly, you could always just upgrade your hardware! Time and money wasted improving query performance could just be better spent on mroe powerful database servers!
While this is a very surface-level approach to optimizing SQL query performance, it should do the trick for Data Analyst interviews and Data Science interviews at SentinelOne. Data Engineers should know a bit more about the and before the interview.
SQL Question 4: Average Threats Detected Per Device
SentinelOne is a cybersecurity firm that protects devices from cyber threats. For this question, you are given two tables: "devices" and "threats". The "devices" table contains information about each device that has been registered in the SentinelOne platform. The "threats" table contains information about each threat detected on a device. Your task is to write a PostgreSQL query to find the average number of threats detected per device.
Example Input:
| device_id | user_id | os | register_date |
|---|---|---|---|
| 001 | 23 | Windows | 2021-03-15 |
| 002 | 45 | Mac | 2021-05-20 |
| 003 | 67 | Linux | 2021-06-10 |
| 004 | 23 | Windows | 2021-07-15 |
| 005 | 89 | Windows | 2021-08-25 |
Example Input:
| threat_id | device_id | threat_type | detection_date |
|---|---|---|---|
| 101 | 001 | Malware | 2021-03-16 |
| 102 | 002 | Ransomware | 2021-05-21 |
| 103 | 003 | Malware | 2021-06-11 |
| 104 | 001 | Spyware | 2021-07-16 |
| 105 | 004 | Malware | 2021-09-18 |
| 106 | 005 | Ransomware | 2021-10-15 |
| 107 | 002 | Spyware | 2021-10-30 |
Answer:
This query first joins the "devices" and "threats" table on the "device_id" column. It then uses a subquery to count the total number of threats detected for each device. Finally, it calculates the average number of threats detected per device using the AVG aggregate function. By using LEFT JOIN, we ensure that even devices with no detected threats are included in the calculation of the average.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for computing averages from given data or this Amazon Average Review Ratings Question which is similar for calculating averages.
SQL Question 5: What do the SQL commands / 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 stress about knowing which DBMS supports what exact commands since the interviewers at SentinelOne should be lenient!).
For a tangible example in PostgreSQL, suppose you were doing an HR Analytics project for SentinelOne, and had access to SentinelOne'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 6: Calculating Click-Through Rates for SentinelOne Ads
SentinelOne runs digital ads to promote their products. Each time an ad is served (displayed on a user's device), it is logged in the table. Each time a user clicks on an ad, it is logged in the table. For each ad, SentinelOne wants to calculate the proportion of times it was clicked when it was served, otherwise known as the click-through rate (CTR).
The table has the columns (the unique identifier of the ad) and (the time the ad was served). The table has the columns (the unique identifier of the ad) and (the time the ad was clicked).
Calculate the CTR for each ad that was served in the year 2022.
Example Input:
| ad_id | serve_time |
|---|---|
| 101 | 2022-01-08 12:57:22 |
| 102 | 2022-01-15 16:32:11 |
| 101 | 2022-01-22 20:17:11 |
| 103 | 2022-02-05 10:10:10 |
Example Input:
| ad_id | click_time |
|---|---|
| 101 | 2022-01-08 12:59:05 |
| 101 | 2022-01-22 20:21:37 |
Answer:
This query joins the and tables on . The operation ensures that even ads that did not receive any clicks are included in the final result, with a CTR of 0.
The click-through rate (CTR) is calculated by dividing the number of clicks by the number of times the ad was served. The COUNT function is used to obtain these quantities.
Since COUNT function returns integer values, we multiply the numerator with to ensure that the division result is a floating point number accurate to the decimal. Finally, the results are grouped by to calculate the CTR for each individual ad.
To solve a related problem on DataLemur's free online SQL code editor, try this SQL interview question asked by Facebook:
SQL Question 7: How does a cross join differ from a natural join?
Cross joins and natural joins are two types of JOIN operations in SQL that are used to combine data from multiple tables. A cross join creates a new table by combining each row from the first table with every row from the second table, and is also known as a cartesian join. On the other hand, a natural join combines rows from two or more tables based on their common columns, forming a new table. One key difference between these types of JOINs is that cross joins do not require common columns between the tables being joined, while natural joins do.
Here's an example of a cross join:
If you have 20 products and 10 colors, that's 200 rows right there!
Here's a natural join example using two tables, SentinelOne employees and SentinelOne managers:
This natural join returns all rows from SentinelOne employees where there is no matching row in managers based on the column.
SQL Question 8: Analyzing Customer Purchase Frequency and Favorite Product
As a data analyst for SentinelOne, one of your responsibilities is to understand the habits of the customers. You need to analyze the customer database and the purchase history table.
Write a SQL query to find out the number of purchases made by each customer and identify their most frequently purchased product. If there are multiple such products for a customer, return any one of them.
Assume we have two tables: and .
Example Input:
| customer_id | name | |
|---|---|---|
| 1 | John Doe | johndoe@example.com |
| 2 | Jane Smith | janesmith@example.com |
| 3 | Mary Johnson | maryjohnson@example.com |
Example Input:
| purchase_id | customer_id | product | purchase_date |
|---|---|---|---|
| 1 | 1 | Antivirus | 2022-10-18 |
| 2 | 2 | Endpoint Protection | 2022-09-12 |
| 3 | 1 | Antivirus | 2022-08-20 |
| 4 | 1 | Firewall | 2022-08-15 |
| 5 | 3 | Antivirus | 2022-06-30 |
Example Output:
| customer_id | purchase_count | favorite_product |
|---|---|---|
| 1 | 3 | Antivirus |
| 2 | 1 | Endpoint Protection |
| 3 | 1 | Antivirus |
Answer:
This query joins the 'customers' table with the 'purchases' table using a standard SQL operation. It then groups the results by 'customer_id' and 'product'. The function counts the number of times each 'product' is connected to each 'customer_id', resulting in the number of purchases each customer has made and their favorite product. The clause sorts the results firstly by the 'purchase_count' in descending order and then by 'customer_id' in ascending order. If a customer has multiple favorite products, this query will return any one of them.
Since joins come up frequently during SQL interviews, take a stab at this Snapchat Join SQL question:
SQL Question 9: Calculate the net floor area of each building
In the real estate management module of SentinelOne's software, all buildings have multiple floors, and each floor has multiple rooms of different sizes. Some rooms may not be usable due to maintenance or other issues.
Write an SQL query which calculates the total floor area of each building, excluding the area of unusable rooms. Round the answer to 2 decimal places. Use the SQRT function to calculate the area of a room, assuming each room is square shaped with side length equal to the 'side_length' field.
The table contains information about each building:
Example Input:
| building_id | name |
|---|---|
| B-101 | One World Center |
| B-102 | Royal Plaza |
| B-103 | Imperial Tower |
The table contains information about each floor of the building:
Example Input:
| floor_id | building_id |
|---|---|
| F-201 | B-101 |
| F-202 | B-101 |
| F-203 | B-102 |
| F-204 | B-103 |
| F-205 | B-103 |
The table contains information about each room of a floor:
Example Input:
| room_id | floor_id | side_length | is_usable |
|---|---|---|---|
| R-301 | F-201 | 10 | 1 |
| R-302 | F-201 | 15 | 1 |
| R-303 | F-202 | 20 | 1 |
| R-304 | F-203 | 20 | 0 |
| R-305 | F-204 | 25 | 1 |
Answer:
The query joins the ,, and tables to accumulate all data necessary for the calculation in the same table. It then filters for only usable rooms (where = 1). The area of each room is calculated as the square of side_length. The total area of each building is the sum of the area of all usable rooms in that building. This result is rounded to 2 decimal places, to get a final result in a proper format.
To practice a very similar question try this interactive Amazon Maximize Prime Item Inventory Question which is similar for calculating space utilization or this Amazon Server Utilization Time Question which is similar for calculating total usage time.
Preparing For The SentinelOne SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
In addition to solving the earlier SentinelOne SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Google, Uber, and Microsoft.
Each exercise has multiple hints, full answers and best of all, there is an online SQL coding environment so you can easily right in the browser your query and have it graded.
To prep for the SentinelOne SQL interview it is also wise to solve interview questions from other tech companies like:
However, if your SQL foundations are weak, forget about going right into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL topics like handling date/timestamp data and filtering data with WHERE – both of these come up often during SQL interviews at SentinelOne.
SentinelOne Data Science Interview Tips
What Do SentinelOne Data Science Interviews Cover?
In addition to SQL query questions, the other topics covered in the SentinelOne Data Science Interview are:
- Statistics Interview Questions
- Python or R Programming Questions
- Open-Ended Data Case Studies
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for SentinelOne Data Science Interviews?
The best way to prepare for SentinelOne 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 Crash Course covering Stats, ML, & Data Case Studies
- Great Reviews (900+ 5-star reviews on Amazon)