9 Trade Desk SQL Interview Questions (Updated 2024)
At Trade Desk, SQL is used across the company to query their Vertica databases. Trade Desk analysts use SQL for analyzing large datasets to optimize ad trading strategies and reporting advertising performance to ad suppliers and demand-side platforms (DSPs). Because of this, Trade Desk almost always asks SQL problems during interviews for Data Science and Data Engineering positions.
To help you study for the Trade Desk SQL interview, here’s 9 Trade Desk SQL interview questions – how many can you solve?
9 Trade Desk SQL Interview Questions
SQL Question 1: Analyzing Ad Campaign Performance
As a data analyst for Trade Desk, you are tasked with analyzing the performance of our ad campaigns over the last quarter. Each ad campaign is characterized by its ID and has delivered various ads to a number of subscribed customers.
You must write a SQL query to find the total views of each ad campaign per month, along with the percentage of increase or decrease in views compared to the previous month. Note that if there is no data for the previous month, you should display null.
For simplicity, assume each row in the table represents a single view of an ad by a customer.
Example Input:
| view_id | ad_campaign_id | view_date |
|---|---|---|
| 1 | 650 | 2022-04-02 |
| 2 | 235 | 2022-04-09 |
| 3 | 650 | 2022-05-02 |
| 4 | 299 | 2022-05-20 |
| 5 | 235 | 2022-05-21 |
| 6 | 650 | 2022-06-30 |
| 7 | 299 | 2022-06-01 |
| 8 | 235 | 2022-06-30 |
Example Output:
| month | ad_campaign_id | total_views | percentage_change_previous_month |
|---|---|---|---|
| 4 | 650 | 1 | NULL |
| 4 | 235 | 1 | NULL |
| 5 | 650 | 1 | -50.00 |
| 5 | 235 | 1 | 0.00 |
| 5 | 299 | 1 | NULL |
| 6 | 650 | 1 | 0.00 |
| 6 | 235 | 1 | 0.00 |
| 6 | 299 | 1 | 0.00 |
Answer:
This query utilizes window functions to compare views per month for each campaign with the previous month. It calculates both the total views and the percentage of change in total views compared to the previous month. Note the use of to capture all preceding rows up to the row previous to the current one, which allows us to compute the percentage of change from the previous month.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Analyzing Advertisement Performance
As a data analyst for Trade Desk, a vital part of your job is to monitor and evaluate the performance of various advertisements published by different ad agencies. Each agency may run multiple advertisements which can be text, banner or video. They may run on various platforms such as websites, apps and social media.
You are given two tables and .
table has columns , , and .
table has columns , , , and where clicked is a BOOLEAN indicating whether the ad was clicked or not.
The task is to write a query that identifies the ad agency with the highest click-through-rate (CTR) for each ad type in the previous month. CTR is calculated as the number of clicks divided by the number of impressions.
Example Input:
| ad_id | ad_type | agency_id | platform_type |
|---|---|---|---|
| 101 | Banner | A1 | Website |
| 102 | Text | A2 | Social Media |
| 103 | Video | A1 | Apps |
| 104 | Banner | A2 | Website |
| 105 | Text | A1 | Social Media |
Example Input:
| impression_id | ad_id | timestamp | user_id | clicked |
|---|---|---|---|---|
| 10001 | 101 | 2022-06-20 10:00:00 | U1 | True |
| 10002 | 102 | 2022-06-21 11:00:00 | U2 | False |
| 10003 | 101 | 2022-06-22 12:00:00 | U3 | False |
| 10004 | 103 | 2022-06-23 13:00:00 | U4 | True |
| 10005 | 104 | 2022-06-24 14:00:00 | U5 | True |
Answer:
This query first joins the and tables based on and then filters the impressions from the previous month. It then groups the data by and and calculates the CTR. The clause sorts the results in descending order of the CTR.
SQL Question 3: What do primary keys do?
A primary key is a column or group of columns that uniquely identifies a row in a table. For example, say you had a database of Trade Desk marketing campaigns data:
In this Trade Desk example, the CampaignID column is the primary key of the MarketingCampaigns table. The constraint ensures that no two rows have the same CampaignID. This helps to maintain the integrity of the data in the table by preventing duplicate rows.
SQL Question 4: Filter Customer Records for Trade Desk
Trade Desk is a data-driven advertisement company. They have a database of information that includes the customer's ID, the ads they've clicked on, the times they've clicked, and the type of ad (video, display, etc.).
They want to find all the customers who have clicked on more than one type of ad and whose total click times is more than 10 within the past month. Create a SQL command that filters and outputs the required data pertaining to customer ID, unique ad type count and total click times.
Example Input:
| log_id | customer_id | click_time | ad_type |
|---|---|---|---|
| 101 | 3254 | 2022-08-29 14:09:52 | video |
| 102 | 1036 | 2022-08-27 10:19:43 | display |
| 103 | 3254 | 2022-08-28 08:30:22 | display |
| 104 | 3254 | 2022-08-28 14:45:37 | video |
| 105 | 1036 | 2022-08-29 18:24:32 | video |
| 106 | 1036 | 2022-08-29 06:14:15 | display |
Example Output:
| customer_id | unique_ad_type_count | total_click_times |
|---|---|---|
| 3254 | 2 | 3 |
| 1036 | 2 | 3 |
Answer:
This SQL command will filter out the logs from the past month using the WHERE clause. It then groups the data by customer ID, counting the number of different ad types and the total number of clicks for each customer using the COUNT function. The HAVING clause filters the result to only include customers that have clicked on more than one type of ad and had more than 10 clicks.
SQL Question 5: How can you select unique records from a table?
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of Trade Desk employees:
| first_name | job_title |
|---|---|
| Akash | Data Analyst |
| Brittany | Data Scientist |
| Carlos | Data Engineer |
| Diego | Data Engineer |
| Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at Trade Desk:
| job_title |
|---|
| Data Analyst |
| Data Scientist |
| Data Engineer |
SQL Question 6: Calculate Click-through Conversion Rates
Trade Desk, a digital advertising company, uses click-through rates (CTR) to evaluate the effectiveness of its ad campaigns. CTR is the ratio of users who click on a specific link to the number of total users who view a page or email. Higher click-through rate means higher engagement with the advertised product.
You, as an analyst, are approached by the marketing department to aid them in an ad campaign assessment. They want to know the click-through conversion rate of each ad campaign, that is, the proportion of users who clicked on a given ad and then added the advertised product to their shopping cart.
You're given two datasets:
:
| ad_campaign_id | user_id | ad_click_timestamp |
|---|---|---|
| 001 | 123 | 06/08/2022 00:00 |
| 002 | 265 | 06/10/2022 00:00 |
| 001 | 362 | 06/18/2022 00:00 |
| 003 | 192 | 07/26/2022 00:00 |
| 003 | 981 | 07/05/2022 00:00 |
:
| user_id | product_id | add_to_cart_timestamp |
|---|---|---|
| 123 | 50001 | 06/08/2022 01:00 |
| 265 | 69852 | 06/10/2022 02:00 |
| 192 | 69852 | 07/26/2022 01:30 |
| 981 | 50001 | 07/05/2022 00:45 |
Answer:
Considering 2 hours as the conversion window (a user adds a product to the cart within two hours after seeing the ad), this PostgreSQL query calculates the CTR conversion rate for ad campaigns:
This query measures the click-through conversion rate by counting the number of users who add a product to the cart within a reasonable time window after clicking an ad (expressed as the ratio to the total number of users who clicked the ad). The ensures all ad clicks are counted, even if they don't result in a "conversion" (user adding a product to the cart).
To practice a similar problem about calculating rates, try this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:
SQL Question 7: What does do, and when would you use this SQL command?
When using , only rows that are identical in both sets will be returned.
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Trade Desk, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the command:
SQL Question 8: Filtering Customer Records
Trade Desk is a technology company that sells advertising technology. For this question, you are given a table that contains , , , , and among other data.
Write an SQL query that will filter all records of customers that have an email address from 'gmail.com' and have clicked on an ad more than ten times. Assume that the 'ad_clicks' field stores the total number of times a user has clicked an ad.
Example Input:
| first_name | last_name | ad_clicks | last_login | |
|---|---|---|---|---|
| John | Doe | john.doe@gmail.com | 15 | 2022-06-01 13:45:00 |
| Jane | Deer | janed@yahoo.com | 23 | 2022-06-15 09:00:00 |
| Tom | Thumb | tom.thumb@gmail.com | 9 | 2022-05-19 19:30:00 |
| Linda | Lovelace | linda.lovelace@hotmail.com | 12 | 2022-07-22 18:20:00 |
| Steve | Madden | steve.m@gmail.com | 11 | 2022-08-12 08:05:00 |
Answer:
This is the PostgreSQL query that selects all columns from the table where the email addresses end with 'gmail.com' (as depicted by the pattern '%gmail.com') and the user has clicked on more than 10 adverts. With this query, the records returned would be those of customers John Doe and Steve Madden.
SQL Question 9: Analyze customer behavior through SQL queries
You are currently an analyst at Trade Desk. You have the following two tables:
| customer_id | customer_name | created_at | |
|---|---|---|---|
| C101 | John Smith | johnsmith@email.com | 09/08/2021 00:00:00 |
| C102 | Neil Armstrong | neil@email.com | 10/10/2021 00:00:00 |
| C103 | Amanda Will | amanda@email.com | 11/15/2021 00:00:00 |
| C104 | Tim Cook | tim@email.com | 5/17/2021 00:00:00 |
| order_id | customer_id | product_id | order_value | order_date |
|---|---|---|---|---|
| O501 | C101 | P6903 | 200 | 08/31/2022 00:00:00 |
| O502 | C102 | P6732 | 350 | 09/15/2022 00:00:00 |
| O503 | C103 | P6190 | 400 | 09/18/2022 00:00:00 |
| O504 | C104 | P6903 | 200 | 09/15/2022 00:00:00 |
Write a SQL query that finds the total order value for each customer along with their email addresses. Sort the result by the total order value in descending order.
Answer:
This query first does a LEFT JOIN on the Customers and Orders table with the common field of customer_id. It then groups the result by email and calculates the total order value for each email by summing up the order values. Finally, it sorts the result in descending order of total_order_value. This gives us a sorted list of customers and their total order values.
Because joins come up frequently during SQL interviews, try an interactive SQL join question from Spotify:
How To Prepare for the Trade Desk 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. Beyond just solving the earlier Trade Desk SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each exercise has multiple hints, full answers and crucially, there is an online SQL coding environment so you can instantly run your query and have it executed.
To prep for the Trade Desk SQL interview you can also be helpful to solve interview questions from other ad-tech companies like:
In case your SQL query skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL tutorial.
This tutorial covers SQL concepts such as filtering on multiple conditions using AND/OR/NOT and Subquery vs. CTE – both of which show up often during Trade Desk SQL assessments.
Trade Desk Data Science Interview Tips
What Do Trade Desk Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems to prepare for the Trade Desk Data Science Interview are:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product-Sense and Business Case Questions
- ML Interview Questions
- Behavioral Interview Questions
How To Prepare for Trade Desk Data Science Interviews?
To prepare for Trade Desk Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
- a crash course on Stats, SQL & ML
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