9 Darktrace SQL Interview Questions (Updated 2024)
At Darktrace, SQL is used across the company for extracting and analyzing cybersecurity data, and managing databases related to artificial intelligence-driven threat detection. That's the reason behind why Darktrace almost always evaluates jobseekers on SQL query questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you prepare for the Darktrace SQL interview, we've curated 9 Darktrace SQL interview questions – able to solve them?
9 Darktrace SQL Interview Questions
SQL Question 1: Analysing Darktrace VIP Users
Darktrace, being a world leading AI cybersecurity platform, has a large number of users. As a Data Analyst at Darktrace, you have been tasked to identify VIP users. A VIP user in this case is defined as any user who has detected more than 20 potential threats in a month.
Here's what our database tables look like:
Example Input:
| activity_id | user_id | activity_date | threat_id | action |
|---|---|---|---|---|
| 1001 | 123 | 2022-06-08 | 50001 | Detected |
| 1002 | 265 | 2022-06-10 | 69852 | Ignored |
| 1003 | 362 | 2022-06-18 | 60005 | Detected |
| 1004 | 123 | 2022-06-20 | 50002 | Detected |
| 1005 | 265 | 2022-06-23 | 69853 | Detected |
| 1006 | 123 | 2022-06-24 | 50003 | Detected |
Now, write a PostgreSQL query to identify these VIP users.
Answer:
This query segments the data by user and month-year, and counts the number of potential threats detected by each user. The HAVING clause then filter out any users who have detected more than 20 threats in a month, listing them in descending order of the number of threats they detected.
To solve a related super-user data analysis question on DataLemur's free interactive SQL code editor, try this recently asked Microsoft SQL interview question:
SQL Question 2: Analyze Network Traffic Patterns
Darktrace is a company that deals with cyber security and uses AI to detect and respond to cyber threats in real time. A common type of data they might deal with is network traffic log data. For this scenario, let's say Darktrace needs to analyze the network traffic patterns from different devices in a particular company.
You are provided with a database table named which records details of all network traffic in the company. Each row represents a network activity indicating the device that generated the traffic, the time it was generated, the amount of data consumed by that activity in megabytes, and whether it was flagged as suspicious or not.
The problem statement is to write a SQL query which calculates the total and average data usage for each device on a rolling weekly basis, along with the count of suspicious activities from that device for the past week.
Example Input:
| log_id | device_id | activity_time | data_used_mb | is_suspicious |
|---|---|---|---|---|
| 1872 | 101 | 2022-06-08 05:30:00 | 600 | 0 |
| 2308 | 102 | 2022-06-10 07:20:00 | 200 | 0 |
| 4127 | 101 | 2022-06-15 12:15:00 | 400 | 1 |
| 5614 | 103 | 2022-06-16 21:45:00 | 350 | 1 |
| 6319 | 101 | 2022-06-21 11:00:00 | 250 | 0 |
Example Output:
| device_id | week_start | total_data_used | avg_data_used | suspicious_activity_count |
|---|---|---|---|---|
| 101 | 2022-06-07 | 600 | 600.00 | 0 |
| 101 | 2022-06-14 | 650 | 325.00 | 1 |
| 101 | 2022-06-21 | 250 | 250.00 | 0 |
| 102 | 2022-06-07 | 200 | 200.00 | 0 |
| 103 | 2022-06-14 | 350 | 350.00 | 1 |
Answer:
In the above SQL query, we've used window functions to calculate the sum and average of data used for each device on a weekly rolling basis. This is done by partitioning the data by device and ordering it by week start (calculated from activity time using the function). Similarly, we keep count of suspicious activities in a week using the window function along with the FILTER clause to count only rows where is '1'. Finally, we filter the output to consider only the last week data.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: Could you clarify the difference between a left and a right join?
Both left and right joins in SQL allow you to combine data from different tables based on a shared key or set of keys. For a concrete example of the difference between these two join types, say you had sales data exported from Darktrace's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
: retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
- : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
SQL Question 4: Average Detection Time
Darktrace is a cyber AI security company, let's say they have a database that keeps track of cyber threats their system detects. Each cyber threat detection is logged with a detection_id, an attack_type, the system_id it was detected on, and the time_to_detect in minutes - which logs how long it took their system to detect the threat.
They wish to figure out the average time it takes for their system to identify different types of cyber threats.
The Darktrace team wants you to answer the following question: What is the average detection time for each type of cyber threat?
Example Input:
| detection_id | attack_type | system_id | time_to_detect |
|---|---|---|---|
| 101 | Trojan | 1 | 5 |
| 102 | Phishing | 2 | 3 |
| 103 | Spam | 1 | 1 |
| 104 | Trojan | 2 | 7 |
| 105 | Spam | 1 | 2 |
Example Output:
| attack_type | avg_time_to_detect |
|---|---|
| Trojan | 6 |
| Phishing | 3 |
| Spam | 1.5 |
Answer:
In this PostgreSQL query, I grouped the rows by attack_type and for each type, I calculated the average detection time. The AVG function provided by SQL will calculate the average of a set of numbers. In this case, it calculates the average time_to_detect for each attack type.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for its focus on calculating total system usage or this Stripe Repeated Payments Question which is similar for its emphasis on identifying and counting specific system events.
SQL Question 5: What's an index, and what are the different types?
In a database, an index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and the use of more storage space to maintain the index data structure.
There are several types of indexes that can be used in a database:
- Primary index: a unique identifier for each row in a table and is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table. It does not allow duplicate values to be inserted into the indexed columns.
- Composite index: is created on multiple columns of a table. It can be used to speed up the search process on the combination of columns.
- Clustered index: determines the physical order of the data in a table. There can be only one clustered index per table.
- Non-clustered index: does NOT determine the physical order of the data in a table. A table can have multiple non-clustered indexes.
SQL Question 6: Calculate Click-Through-Rates for Darktrace's Promotions
As a part of its marketing efforts, Darktrace runs various promotions on its website. The team tracks the number of users who view each promotion and the number of users who click on each promotion. Calculate the Click-Through Rate (CTR) for each promotion. CTR is defined as the number of "Clicks" divided by the number of "Views" fat each promotion.
Example Input:
| promotion_id | promotion_name |
|---|---|
| 1 | "Spring Sale" |
| 2 | "Memorial Day Special" |
| 3 | "Summer Kickoff" |
Example Input:
| event_id | promotion_id | event_type | timestamp |
|---|---|---|---|
| 1001 | 1 | "View" | 05/01/2022 00:00:00 |
| 1002 | 1 | "Click" | 05/01/2022 01:00:00 |
| 1003 | 2 | "View" | 05/25/2022 00:00:00 |
| 1004 | 2 | "Click" | 05/25/2022 02:00:00 |
| 1005 | 2 | "View" | 05/25/2022 03:00:00 |
| 1006 | 3 | "View" | 06/01/2022 00:00:00 |
Expected Output:
| promotion_name | ctr |
|---|---|
| "Spring Sale" | 1.00 |
| "Memorial Day Special" | 0.50 |
| "Summer Kickoff" | 0.00 |
Answer:
This query first determines whether each event is a 'View' or 'Click'. It then takes the sum of 'Clicks' divided by the sum of 'Views' to compute the CTR. The COALESCE and NULLIF functions are used to handle cases where there are no 'Views' for a promotion, thus avoiding a division by zero, and ensuring that the CTR becomes 0 in such cases. It finally groups by the promotion_name to provide the CTR per promotion.
To solve a similar problem about calculating rates, try this TikTok SQL question on DataLemur's online SQL coding environment:
SQL Question 7: Can you explain the distinction 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 8: Finding Customer Records with Specific Keywords
Darktrace is a cybersecurity company that deals with thousands of cyber threat records. For their internal use, they have a database table, , consisting of , , and .
Design an SQL query that would allow Darktrace to find all the records where the threat description includes the word 'phishing'.
Example Input:
| record_id | incident_date | threat_type | threat_description |
|---|---|---|---|
| 1191 | 02/05/2021 | Malware | System infected with known malware |
| 1235 | 06/05/2021 | Malware | Suspicious file detected |
| 2257 | 07/10/2021 | Phishing | Phishing attack detected |
| 5263 | 12/15/2021 | DDoS | Large-scale DDoS attack |
| 6951 | 01/05/2022 | Phishing | Email phishing attempt reported |
Example Output:
| record_id | incident_date | threat_type | threat_description |
|---|---|---|---|
| 2257 | 07/10/2021 | Phishing | Phishing attack detected |
| 6951 | 01/05/2022 | Phishing | Email phishing attempt reported |
Answer:
This query searches and returns all records in the table where the includes the word 'phishing'. It uses the operator in SQL which allows for pattern matching using wildcard characters - in this case, the percentage symbol '%'. This symbol represents zero, one or multiple characters which means that any strings before or after 'phishing' will be included.
SQL Question 9: Analyze Customer Purchasing Behavior
Given a database of customer orders and a product catalog in the company Darktrace, write a SQL query to analyze the purchasing behavior of customers. Specifically, find the average order cost of each customer ordered by descending order of cost. Assume the relevant tables are named and , with the table containing a foreign key relating to the table.
The table has the following structure:
Example Input:
| order_id | customer_id | order_date | product_id | quantity |
|---|---|---|---|---|
| 9512 | 567 | 06/08/2022 | 22501 | 3 |
| 7023 | 234 | 06/10/2022 | 64312 | 2 |
| 8811 | 854 | 06/18/2022 | 22501 | 1 |
| 7453 | 123 | 07/26/2022 | 64312 | 5 |
| 8365 | 567 | 07/05/2022 | 64312 | 6 |
The table has the following structure:
Example Input:
| product_id | product_name | price |
|---|---|---|
| 22501 | Darktrace Antigena | 500 |
| 64312 | Darktrace Industrial Immune System | 700 |
You need to join these two tables on the shared column to get the cost of each customer's orders (by multiplying the quantity ordered by the product price), then take the average for each customer.
Answer:
This query first performs a join on the orders and products tables based on the column that exists in both tables. It then groups the data by as we are interested in averages on a per-customer basis. The aggregate function is used to calculate the average order cost for each customer, which is achieved by multiplying the quantity of each product ordered by the price of that product. The results are then ordered in descending order to give us the customers with the highest average order cost first.
Because joins come up so often during SQL interviews, practice this Snapchat SQL Interview question using JOINS:
Darktrace SQL Interview Tips
The key to acing a Darktrace SQL interview is to practice, practice, and then practice some more!
In addition to solving the earlier Darktrace SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Netflix, Google, and Amazon.
Each problem on DataLemur has hints to guide you, detailed solutions and crucially, there's an interactive coding environment so you can instantly run your SQL query answer and have it graded.
To prep for the Darktrace SQL interview it is also helpful to solve SQL questions from other tech companies like:
However, if your SQL skills are weak, don't worry about going right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as aggregate functions and CASE/WHEN/ELSE statements – both of which pop up routinely in SQL interviews at Darktrace.
Darktrace Data Science Interview Tips
What Do Darktrace Data Science Interviews Cover?
In addition to SQL query questions, the other topics covered in the Darktrace Data Science Interview are:
- Statistics and AB Testing Questions
- Python or R Programming Questions
- Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Darktrace Data Science Interviews?
The best way to prepare for Darktrace Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG & startups
- A Crash Course covering Product Analytics, SQL & ML
- Great Reviews (900+ 5-star reviews on Amazon)