# 10 The Trade Desk SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At The Trade Desk, SQL does the heavy lifting for querying and analyzing advertising datasets, and managing and updating the audience and campaign databases. Unsurprisingly this is why The Trade Desk almost always asks SQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.

In case you're preparing for a SQL Interview, we've curated 10 The Trade Desk SQL interview questions to practice, which are similar to commonly asked questions at The Trade Desk – able to answer them all?

## 10 The Trade Desk SQL Interview Questions

### SQL Question 1: Calculate the Daily Average Bid Amount

Let's suppose you are a data analyst at The Trade Desk, and you're given a dataset that records all the bids that the company placed in the last quarter. The data includes the date, campaign_ID, and bid amount. Your task is to write a SQL query to calculate the daily average bid amount per campaign for the last quarter.

##### Example Input:
bid_idcampaign_idbid_datebid_amount
227156712/02/20222.50
891278912/02/20223.20
378456712/03/20222.70
742378912/03/20223.10
283756712/04/20222.40

#### Solution:

The SQL window function you can use here is with the use of clause ordering by and partitioned by .

In PostgreSQL, your solution might look like this:

This calculates the average bud amount per campaign for each day in the specified date range, ordered by and . The clause ensures the average at each row is calculated using all the rows till the current one in each partition.

##### Example Output:
bid_datecampaign_iddaily_avg_bid
12/02/20225672.50
12/03/20225672.60
12/04/20225672.53
12/02/20227893.20
12/03/20227893.15

This table indicates the daily average bid amount for each campaign across all the bidding dates in the last quarter.

To practice a similar problem on DataLemur's free online SQL code editor, try this Meta SQL interview question:

### SQL Question 2: Ad Campaign Performance Evaluation

Assuming you are working as a Data Analyst at The Trade Desk, a company that specializes in programmatic advertising. Your company runs ad campaigns for various advertisers. Each campaign has multiple ads and these ads are displayed in different regions. Your goal is to evaluate the effectiveness of these campaigns based on some metrics such as the number of impressions and click-through rates (CTR).

These metrics are derived from the following tables:

11011
21021
31032
41042
51053
1011
1021
1032
1042
1053
##### table:
10106/08/2022 00:00:001
10206/10/2022 00:00:000
10306/18/2022 00:00:001
10407/26/2022 00:00:000
10507/05/2022 00:00:001

The question here is to write an SQL query to calculate the average click-through rate (CTR) by campaign, advertiser, and region for all ads.

• CTR is defined as the number of clicks divided by the number of impressions (i.e., each time an ad is shown) for each ad, and then you average it by campaign, advertiser, and region.
• You should consider each row in the table as 1 impression.

This query works by first joining the , , and tables on their common keys. It then groups by , , and . Finally, it calculates the average CTR by converting the value to float and taking the average grouped by each campaign, advertiser, and region.

To solve a similar problem about calculating rates, try this SQL interview question from TikTok on DataLemur's online SQL coding environment:

### SQL Question 3: What are the three different normal forms?

The three levels of database normalization (also known as normal forms) are:

First Normal Form (1NF):

• Each column should contain the same type of data (no mixing strings vs. integers)
• Each column in a table contains a single value (no lists or containers of data)
• Each row in the table is unique

Second Normal Form (2NF)

• It's in 1st Normal Form
• All non-key attributes are dependent on the primary key

Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.

Third normal form (3NF) if it meets the following criteria:

• It's in 2nd Normal Form
• There are no transitive dependencies in the table.

A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).

Although there is a 4NF and 5NF, you probably don't need to know it for the The Trade Desk interview.

### SQL Question 4: Filter Customers Based on Campaign Interaction

As part of the marketing analytics team at The Trade Desk, you're required to filter out customers who have both clicked on a specific marketing campaign and made a purchase within 30 days of the campaign. Date of campaign interaction and date of purchase should both be in the year 2022. Use the following tables for this analysis:

##### Example Input:
interaction_idcustomer_idcampaign_idinteraction_date
5487101700207/02/2022 00:00:00
3498234861207/15/2022 00:00:00
9623345700208/20/2022 00:00:00
1274101861209/18/2022 00:00:00
5281876700207/30/2022 00:00:00
##### Example Input:
purchase_idcustomer_idproduct_idpurchase_date
84611015000107/28/2022 00:00:00
23412346985207/28/2022 00:00:00
45273455000109/10/2022 00:00:00
56218766985208/28/2022 00:00:00
98211018985210/01/2022 00:00:00

This PostgreSQL query first joins the and tables on . It then filters for records where the interaction date and the purchase date are both within the year 2022, and the purchase date is within 30 days of the interaction date. This gives us the list of customers who have both clicked on a specific marketing campaign and made a purchase within 30 days of the campaign.

To solve another question about calculating rates, try this SQL interview question from TikTok within DataLemur's online SQL coding environment:

### SQL Question 5: What's the difference between a one-to-one vs. a one-to-many relationship between two entities? Give examples.

When designing a database schema, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a car and a license plate - each car has one license plate, and each license plate belongs to one car.

On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.

### SQL Question 6: Calculate the average bid price per advertising campaign

The Trade Desk is a technology company that empowers buyers of advertising. As an interview question, you may be asked to calculate the average bid price for each advertising campaign from the last week, to get insight on the spending pattern.

##### (Sample Input):
bid_idcampaign_idtimestampbid_price_usd
2611109/07/2022 00:01:001.53
6241209/09/2022 00:01:001.80
6701109/13/2022 02:00:001.70
2101309/11/2022 21:00:002.15
8921109/14/2022 11:01:001.55
7301209/09/2022 15:20:001.77
1021309/13/2022 16:31:002.05
##### Example Output:
campaign_idaverage_bid_price
111.59
121.79
132.10

This query first limits the data to only the bids from the last 7 days using the NOW() function in PostgreSQL combined with INTERVAL '7 days'. It then calculates the average bid price per campaign by using the AVG function rounded to two decimal places for each campaign_id.

To practice a similar problem on DataLemur's free interactive SQL code editor, solve this SQL interview question asked by Facebook:

### SQL Question 7: What is the difference between a primary key and a foreign key?

To explain the difference between a primary key and foreign key, let's start with an example The Trade Desk sales database:

:

+------------+------------+------------+------------+ | order_id | product_id | customer_id| quantity | +------------+------------+------------+------------+ | 1 | 303 | 1 | 2 | | 2 | 404 | 1 | 1 | | 3 | 505 | 2 | 3 | | 4 | 303 | 3 | 1 | +------------+------------+------------+------------+

In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.

and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.

### SQL Question 8: Find Customers From Specific Country

The Trade Desk is a global technology company that markets a software platform used by digital ad buyers to purchase data-driven advertising campaigns across various ad formats and devices.

Consider the following data from our company's customer records. The task is to find all customers from United States.

#### Example Input:

customer_idfirst_namelast_namecountrydate_created
1234JohnDoeUnited States01/03/2020 00:00:00
9012PeterParkerUnited States12/24/2019 00:00:00
3456BruceWayneUnited Kingdom11/18/2020 00:00:00
7890TonyStarkUnited States05/28/2020 00:00:00

#### Example Output:

customer_idfirst_namelast_namecountry
1234JohnDoeUnited States
9012PeterParkerUnited States
7890TonyStarkUnited States

This SQL query selects all the records from the table where the country is 'United States'. The SQL keyword is used in the clause to search for a specified pattern in a column. Here, we're looking for an exact match, and we're retrieving all columns () for those records.

To solve another question about calculating rates, solve this SQL interview question from TikTok within DataLemur's online SQL coding environment:

### SQL Question 9: Average Spend of Customers on Different Campaigns

You're a data scientist at The Trade Desk, a data-driven advertising company. You have a table that stores customer details and a table that logs details of ad campaigns a customer was targeted with, along with how much they spent as a result of each campaign.

The table looks like this:

##### Example Input:
customer_idfirst_namelast_nameemail

The table looks like this:

##### Example Input:
campaign_idcustomer_idcampaign_namespend
1013145Winter Sale120.50
1023145Spring Sale200.75
1038257Summer Sale354.80
1042649Autumn Sale198.35
1059345Black Friday287.40

Write a SQL query that joins these two tables and calculates the average spend of each customer across all campaigns they were targeted with. Order the result by average spend in descending order.

This query first joins the and tables on the field. Then, it groups the result by , , and and calculates the average for each group. Finally, the result is ordered by in descending order, meaning the customers with the highest average spend across all campaigns will appear first.

To solve a similar problem about calculating rates, try this SQL interview question from TikTok within DataLemur's online SQL code editor:

### SQL Question 10: What's the difference between and clause?

The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.

For example, say you were analyzing salaries for analytics employees at The Trade Desk:

This query retrieves the total salary for each Analytics department at The Trade Desk and groups the rows by the specific department (i.e. ""Marketing Analytics"", ""Business Analytics"", ""Sales Analytics"" teams).

The clause then filters the groups to include only The Trade Desk departments where the total salary is greater than \$1 million

### How To Prepare for the The Trade Desk SQL Interview

The key to acing a The Trade Desk SQL interview is to practice, practice, and then practice some more! Besides solving the above The Trade Desk SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Microsoft, Google, Amazon, and tech startups.

Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it graded.

To prep for the The Trade Desk SQL interview it is also wise to solve SQL problems from other tech companies like:

But if your SQL foundations are weak, don't worry about diving straight into solving questions – go learn SQL with this SQL tutorial for Data Analytics.

This tutorial covers SQL topics like CASE/WHEN statements and LEAD window function – both of which come up routinely in The Trade Desk SQL interviews.

### The Trade Desk Data Science Interview Tips

#### What Do The Trade Desk Data Science Interviews Cover?

For the The Trade Desk Data Science Interview, beyond writing SQL queries, the other types of questions to practice:

• Statistics and Probability Questions
• Python or R Programming Questions
• Open-Ended Data Case Studies
• Machine Learning and Predictive Modeling Questions
• Resume-Based Behavioral Questions

#### How To Prepare for The Trade Desk Data Science Interviews?

The best way to prepare for The Trade Desk Data Science interviews is by reading Ace the Data Science Interview. The book's got: