8 Check Point SQL Interview Questions (Updated 2024)
At Check Point Software, SQL is used quite frequently for analyzing cybersecurity threats and patterns, and managing large-scale database systems for software bug tracking. They also support secure SQL database access for their customers, as part of their zero-trust DB service. That's why Check Point Software often asks SQL interview questions when evaluating folks fro Data Analyst, Data Science, and BI jobs.
So, to help you practice for the Check Point SQL interview, here’s 8 Check Point Software SQL interview questions – how many can you solve?
8 Check Point Software SQL Interview Questions
SQL Question 1: Identifying Power Users of Check Point
Check Point is a multinational provider of software and hardware products for IT security, including network security, endpoint security, cloud security, mobile security, data security and security management.
As Check Point's business analyst, you are tasked with identifying the most important users of the Check Point's network security products. These power users log in to the service very frequently, often daily. Please write a SQL query to identify users who have logged in to the service at least 25 times in the last month.
Example Input:
| log_id | user_id | log_date |
|---|---|---|
| 6171 | 123 | 06/08/2022 |
| 7802 | 123 | 06/10/2022 |
| 5293 | 123 | 06/11/2022 |
| 6352 | 123 | 06/12/2022 |
| 4517 | 123 | 06/13/2022 |
| 9218 | 265 | 06/10/2022 |
| 4875 | 265 | 06/11/2022 |
| 6254 | 362 | 06/18/2022 |
Answer:
This query will look at the table and count the number of unique logins per user. It then filters this list to only include users who have logged in 25 or more times within the time period from June 1, 2022 to June 30, 2022. The result will be a list of s which correspond to the power users for Check Point within this month.
To practice a related super-user data analysis question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:
SQL Question 2: Identify the most popular products sold each month
As an analyst at Checkpoint, it is important to understand how different products are performing over time. To provide insights, can you write a SQL query to identify the products with the highest total quantity sold for each month? Assume that the months are identified by integers from 1(January) to 12(December). Use the "sales" table that contains the following columns:
- sale_id: The unique identifier for each transaction.
- product_id: The unique identifier for each product.
- quantity: The quantity of the product sold in the transaction.
- sale_date: The date the transaction occurred.
Example Input:
| sale_id | product_id | quantity | sale_date |
|---|---|---|---|
| 1 | 1001 | 3 | 2021-06-01 |
| 2 | 2001 | 2 | 2021-06-02 |
| 3 | 1001 | 1 | 2021-06-18 |
| 4 | 3001 | 5 | 2021-07-04 |
| 5 | 3001 | 1 | 2021-07-05 |
Write a SQL query that returns the product_id with the highest total quantity sold for each month. In case of a tie, include all products with the highest total quantity.
Expected Output:
| mth | product_id | total_qty |
|---|---|---|
| 6 | 1001 | 4 |
| 7 | 3001 | 6 |
Answer:
This query uses the function to get the month from the sale_date, and the function to calculate the total quantity sold for each product per month. The result is ordered by month and total quantity in descending order, so within each month, the products with the highest total quantity come first.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 3: What's the SQL command do, and when would you use it?
The SQL command merges the results of multiple statements and keeps only those rows that are present in all sets.
For example, say you were doing an HR Analytics project for Check Point, and had access to Check Point's employees and contractors 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 also show up in the employees table:
SQL Question 4: Filter Customers Based on Purchase History and Location
Consider a table storing all purchases made by the customers of Check Point. Each purchase record has a , the of the customer making the purchase, the of the item purchased, the and the . Another table stores user details including and .
Write a SQL query to find all customers located in 'New York' who have purchased more than 5 items, with a total purchase amount exceeding $500 before August 1, 2022.
Sample Input:
| purchase_id | user_id | product_id | purchase_date | purchase_amount |
|---|---|---|---|---|
| 6171 | 123 | 5 | 06/08/2022 | 150 |
| 7802 | 123 | 3 | 06/10/2022 | 100 |
| 5293 | 362 | 7 | 06/18/2022 | 200 |
| 6352 | 192 | 9 | 07/26/2022 | 50 |
| 4517 | 123 | 1 | 07/05/2022 | 300 |
Sample Input:
| user_id | location |
|---|---|
| 123 | 'New York' |
| 265 | 'Chicago' |
| 362 | 'New York' |
| 192 | 'Los Angeles' |
| 981 | 'New York' |
Answer:
This SQL query works in the following way: first, it joins the table and the table on . It then filters the records to get the purchases made in 'New York' before the specified purchase date. Finally, it uses the clause to group the results by and the clause to filter groups having more than 5 purchases with a total purchase amount over $500.
SQL Question 5: What's the difference between a clustered and non-clustered index?
Here's an example of a clustered index on the column of a table of Check Point payments table:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
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 6: Calculating the Average Number of Detected Threats per Day
At Check Point, a cybersecurity company, every day, their software detects numerous threats for their clients. For a single client, how would you calculate the average number of detected threats per day for a specific month?
Consider the following hypothetical data:
Example Input:
| client_id | threat_id | detect_date |
|---|---|---|
| 543 | 214 | 2022-06-01 |
| 543 | 582 | 2022-06-01 |
| 543 | 497 | 2022-06-02 |
| 543 | 178 | 2022-06-02 |
| 543 | 351 | 2022-06-03 |
| 543 | 922 | 2022-06-10 |
| 543 | 812 | 2022-06-10 |
| 543 | 543 | 2022-06-17 |
| 543 | 426 | 2022-06-17 |
| 543 | 892 | 2022-06-30 |
Example Output:
|client_id|avg_threats| |:----|:----|:----| |543|1.00|
Answer:
This query firstly aggregates the number of threats detected each day for a specific month of a particular year, by using the inner subquery. Then, it calculates the average number of these daily threats for each client, giving us the average number of detected threats per day.
To practice a very similar question try this interactive Google Median Google Search Frequency Question which is similar for calculating a daily average or this Amazon Average Review Ratings Question which is similar for grouping by month.
SQL Question 7: What does the operator do?
For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for Check Point, and had access to Check Point'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:
Note that is available in PostgreSQL and SQL Server, while is the equivalent operator which is available in MySQL and Oracle (but don't worry about knowing which RDBMS supports which exact commands since Check Point interviewers aren't trying to trip you up on memorizing SQL syntax).
SQL Question 8: Cyberattack frequency by Hour of Day
As a security services company, Check Point is constantly monitoring for cyberattacks. They have a table of log entries for cyberattacks detected. For understanding the pattern and prioritizing their resources, they want to know how many cyberattacks take place during each hour of the day. Assume every cyberattack has an entry with a timestamp in the "attacks" table.
Example Input:
| attack_id | timestamp | attack_type | source_ip |
|---|---|---|---|
| 1001 | 2022-10-10 02:45:48 | DDoS | 192.128.3.1 |
| 1002 | 2022-10-10 04:30:37 | DDoS | 192.138.4.2 |
| 1003 | 2022-10-10 04:35:27 | Malware | 192.158.5.3 |
| 1004 | 2022-10-10 15:15:48 | Phishing | 192.178.6.4 |
| 1005 | 2022-10-10 15:55:48 | DDoS | 192.198.7.5 |
Expected Output:
| hour_of_day | attack_count |
|---|---|
| 02 | 1 |
| 04 | 2 |
| 15 | 2 |
Answer:
Here's a PostgreSQL query to solve this problem:
Explanation: The function allows us to isolate the hour component from the timestamp of each attack. This is then grouped by the statement, effectively creating a new grouping for each distinct hour of the day in which attacks occur. The function then counts the number of attacks that occur within each hour of the day grouping. The final result is a set of rows where each row represents an hour of the day and the corresponding number of cyberattacks that occurred during that hour.
Check Point SQL Interview Tips
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 Check Point SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each problem on DataLemur has hints to guide you, detailed solutions and most importantly, there is an online SQL code editor so you can right online code up your query and have it checked.
To prep for the Check Point SQL interview it is also wise to practice SQL problems from other tech companies like:
But if your SQL foundations are weak, don't worry about going right into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers things like math functions like CEIL()/FLOOR() and manipulating string/text data – both of which show up frequently during Check Point interviews.
Check Point Software Data Science Interview Tips
What Do Check Point Data Science Interviews Cover?
In addition to SQL query questions, the other topics to prepare for the Check Point Data Science Interview are:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- ML Modelling Questions
- Behavioral Based Interview Questions
How To Prepare for Check Point Data Science Interviews?
To prepare for Check Point Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Microsoft, Amazon & startups
- a refresher covering Python, SQL & ML
- over 900+ 5-star reviews on Amazon