At Qualys, SQL is used frequently for analyzing vulnerability data across different networks to help their customers reduce their cybersecurity risk. It shouldn't surprise you that Qualys typically asks SQL problems during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you study for the Qualys SQL interview, we'll cover 11 Qualys SQL interview questions – able to answer them all?
Qualys is a provider of cloud security, compliance, and related services, and hence it's likely that its most important users are those who frequently run security scans. You have been given the task to identify such power users.
Let's say all the scans users are running are stored in a table. This table contains information about the user who ran the scan (), when it was run (), the scan type () and the number of vulnerabilities found (). "Power users" can be defined as those users who run scans more than average and find more vulnerabilities than average.
Now, create a SQL query that returns a list of such power users along with the number of scans they have run and vulnerabilities they have found.
scan_id | user_id | scan_date | scan_type | vulnerabilities |
---|---|---|---|---|
4397 | 210 | 02/01/2021 | 'Web App' | 16 |
8475 | 653 | 02/02/2021 | 'Network' | 20 |
3742 | 210 | 02/03/2021 | 'Web App' | 12 |
5793 | 932 | 02/03/2021 | 'Network' | 19 |
8964 | 210 | 02/04/2021 | 'Web App' | 18 |
user_id | scan_count | total_vulnerabilities |
---|---|---|
210 | 3 | 46 |
This query first groups the table by and then counts number of scans and sums up vulnerabilities for each user. It only selects those users, who run scans more than the average scan count and find more vulnerabilities than the average found by all users.
To solve a similar VIP customer analysis question on DataLemur's free interactive SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
Suppose Qualys, a provider of cloud security, compliance and related services, stores vulnerability scan data and you are asked to write a SQL query to calculate the average vulnerability score for each month for each detected threat.
The table in their database has the following columns: (unique identifier for each scan), (unique identifier for each detected threat), (date the scan was run), and (severity score of the detected threat, with 10 being very severe and 0 being not severe).
scan_id | threat_id | scan_date | score |
---|---|---|---|
001 | ABC123 | 2022-01-02 | 8 |
002 | DEF456 | 2022-01-03 | 5 |
003 | DEF456 | 2022-01-15 | 7 |
004 | ABC123 | 2022-02-10 | 9 |
005 | GHI789 | 2022-02-20 | 6 |
Calculate the average score for each threat on a monthly basis.
month | threat | avg_score |
---|---|---|
1 | ABC123 | 8.00 |
1 | DEF456 | 6.00 |
2 | ABC123 | 9.00 |
2 | GHI789 | 6.00 |
This SQL query uses a window function to partition the data by and the month of (extracted using the function). It then computes the average score within each partition (). The clause is used to sort the results by month and threat.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
The primary key of a table is a column or set of columns that serves as a unique identifier for each row. It ensures that all rows are distinct and does not allow null values.
For example, say you had stored some Facebook ad campaign data that Qualys ran:
The CampaignID column is used to uniquely identify each row in the table, and the constraint ensures that there are no duplicate CampaignID values. This helps to maintain the accuracy of the data by preventing duplicate rows. The primary key is also an important part of the table because it enables you to easily identify and reference specific campaigns in your Facebook Ad data. You can use it to join to other tables in the database, such as a table with data on the results of the campaigns.
As a cybersecurity firm, Qualys regularly scans all its systems to detect any new vulnerabilities. For each identified vulnerability, the company generates a unique vulnerability ID, logs the system ID where the vulnerability was found, the severity of the vulnerability, and the detected date.
Design a database table capturing this information. Then, write a SQL query in PostgreSQL that returns a count of all 'Severe' vulnerabilities detected in the last 30 days, grouped by the affected system.
Here are some sample data:
vulnerability_id | system_id | detection_date | severity |
---|---|---|---|
871 | 1 | 06/08/2022 00:00:00 | Moderate |
2512 | 2 | 06/10/2022 00:00:00 | Severe |
993 | 3 | 07/18/2022 00:00:00 | Moderate |
3221 | 1 | 07/28/2022 00:00:00 | Severe |
4227 | 2 | 07/30/2022 00:00:00 | Severe |
Your task is to return a table in this format:
system_id | severe_vulnerabilities |
---|---|
1 | 1 |
2 | 2 |
This query filters the "vulnerabilities" table to only include rows where the vulnerability was marked 'Severe' and was detected within the last month. The clause groups these vulnerabilities by system, and the in the clause counts the total number of severe vulnerabilities for each one, exactly fulfilling the question requirement.
is used to combine the output of multiple statements into one big result!
For a concrete example, say you were doing an HR Analytics project for Qualys and needed to analyze both Qualys's employees and contractors who were hired after 2022 started. You could use in the following way:
As a Cybersecurity company, Qualys needs to frequently check their customer's security status. Assume Qualys has a customer records database and wants to filter customers that have not updated their security patches for two months or more, and customers who are using any of their five main products.
Given the and tables, provide a query that will return the list of customers who fit this criteria.
customer_id | last_patch_date | product_id |
---|---|---|
1 | 2022-06-08 | 101 |
2 | 2022-08-10 | 102 |
3 | 2022-04-18 | 103 |
4 | 2022-05-26 | 104 |
5 | 2022-07-05 | 105 |
product_id | product_name |
---|---|
101 | "Qualys Cloud Platform" |
102 | "Qualys AssetInventory" |
103 | "Qualys VMDR" |
104 | "Qualys Policy Compliance" |
105 | "Qualys Patch Management" |
This will return a list of customer IDs, the name of the product they're using, and their last patch date, only if their last patch date is more than two months ago and they're using one of the main five Qualys products. The connects the and tables on the field. The clause checks both conditions.
Here's some strategies that can generally speed up a slow SQL query:
While there's an entire industry dedicated to database performance tuning , for Data Analyst and Data Science interviews at Qualys, 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.
Given a table tracking clicks on ads, a table tracking occurrences of ads served, and a table tracking occasions where a clicked-on ad led to viewing a product detail page, write a SQL query to calculate the overall Click-Through-Rate (CTR) and the Product View-Through-Rate (PVTR) - the ratio of ads clicked to ads served, and the ratio of ads leading to product views to ads clicked respectively. CTR and PVTR need to be calculated for each ad campaign for the month of July.
click_id | user_id | click_datetime | campaign_id |
---|---|---|---|
101 | 451 | 07/04/2022 14:12:03 | 1001 |
102 | 968 | 07/09/2022 16:32:21 | 1002 |
103 | 784 | 07/14/2022 12:33:45 | 1001 |
104 | 256 | 07/20/2022 18:00:00 | 1002 |
105 | 710 | 07/29/2022 09:08:10 | 1001 |
ad_id | user_id | ad_datetime | campaign_id |
---|---|---|---|
901 | 451 | 07/04/2022 14:11:33 | 1001 |
902 | 968 | 07/09/2022 16:30:12 | 1002 |
903 | 784 | 07/14/2022 12:30:12 | 1001 |
904 | 890 | 07/20/2022 18:00:00 | 1001 |
905 | 123 | 07/29/2022 09:00:00 | 1002 |
view_id | user_id | view_datetime | campaign_id |
---|---|---|---|
801 | 451 | 07/04/2022 14:15:12 | 1001 |
802 | 968 | 07/09/2022 16:40:45 | 1002 |
803 | 710 | 07/29/2022 09:25:50 | 1001 |
In this block of SQL, the window of time is limited to the month of July. In the first clause, the total number of ads served and the total number of ads clicked is determined for each campaign. In the second clause, the total number of ads clicked and the total number of product views is also calculated for each campaign. The final statement merges these two derived tables and calculates the CTR and PVTR for each campaign by dividing the appropriate counts.
To solve a related problem on DataLemur's free online SQL coding environment, solve this Meta SQL interview question:
Qualys, as a provider of cloud security, compliance and related services, largely deals with security vulnerability data. Suppose Qualys has a table named that logs each vulnerability scan performed on a client's system.
Each row represents a single scan, detailing the client (given by ), the date when the scan was carried out (), the type of vulnerability scan (), and the total vulnerabilities found ().
We want to find out the highest number of vulnerabilities found in a single scan for each client, per scan type.
scan_id | client_id | scan_date | scan_type | vulnerabilities_found |
---|---|---|---|---|
001 | 576 | 08/07/2022 | Network | 254 |
002 | 576 | 08/10/2022 | Network | 244 |
003 | 423 | 08/15/2022 | Web | 35 |
004 | 423 | 08/16/2022 | Web | 40 |
005 | 576 | 08/20/2022 | Web | 78 |
006 | 423 | 08/25/2022 | Web | 33 |
client_id | scan_type | max_vulnerabilities |
---|---|---|
576 | Network | 254 |
576 | Web | 78 |
423 | Web | 40 |
This query groups the rows by and . For each group created, it finds and returns the row with the highest number of , as determined by the aggregate function . The clause at the end simply orders the output by for clarity.
Clustered indexes have a special characteristic in that the order of the rows in the database corresponds to the order of the rows in the index. This is why a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between clustered and non-clustered indexes is that the database tries to maintain the order of the data in the database to match the order of the corresponding keys in the clustered index. This can improve query performance as it provides a linear-access path to the data stored in the database.
Given the table, find all the customer records where the email domain is .
customer_id | first_name | last_name | |
---|---|---|---|
27 | John | Smith | johnsmith@qualys.com |
34 | Jane | Doe | janedoe@gmail.com |
63 | Mark | Davis | markdavis@qualys.com |
74 | Cynthia | Johnson | cynthiajohnson@yahoo.com |
83 | Sarah | Powell | sarahpowell@qualys.com |
customer_id | first_name | last_name | |
---|---|---|---|
27 | John | Smith | johnsmith@qualys.com |
63 | Mark | Davis | markdavis@qualys.com |
83 | Sarah | Powell | sarahpowell@qualys.com |
This SQL query will return all the records from the table where the column ends with . In PostgreSQL, the keyword is used to match text string patterns. The symbol in the clause is a wildcard character that matches any sequence of characters. So, matches any string that ends with .
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Qualys SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the above Qualys SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each SQL question has hints to guide you, step-by-step solutions and crucially, there's an online SQL code editor so you can easily right in the browser your SQL query and have it executed.
To prep for the Qualys SQL interview you can also be a great idea to practice interview questions from other tech companies like:
But if your SQL coding skills are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL concepts such as GROUP BY and filtering data with WHERE – both of these pop up routinely in SQL job interviews at Qualys.
In addition to SQL query questions, the other types of questions to prepare for the Qualys Data Science Interview are:
To prepare for Qualys Data Science interviews read the book Ace the Data Science Interview because it's got: