11 LiveRamp SQL Interview Questions (Updated 2024)
At LiveRamp, SQL is often used for creating advertising segments, and joining disparate marketing datasets into a unified dataset. That's why LiveRamp almost always asks SQL query questions during interviews for Data Analyst, Data Science, and BI jobs.
So, to help you ace the LiveRamp SQL interview, we've curated 11 LiveRamp SQL interview questions – can you answer each one?
11 LiveRamp SQL Interview Questions
SQL Question 1: Identify Power Users From LiveRamp Customer Database
Assuming that LiveRamp, a data connectivity platform, values those customers highly who frequently upload data sets. Your task is to write a SQL query that identifies the users who have been uploading data sets more than 100 times in the past month.
Example Input:
| upload_id | user_id | upload_date | dataset_id | size_in_MB |
|---|---|---|---|---|
| 85371 | 123 | 12/09/2022 | 90001 | 350 |
| 99762 | 265 | 12/15/2022 | 48252 | 500 |
| 89233 | 123 | 12/16/2022 | 90001 | 420 |
| 76522 | 192 | 12/20/2022 | 48252 | 300 |
| 84771 | 981 | 12/11/2022 | 48252 | 200 |
Answer:
This PostgreSQL SQL query starts by selecting the user_id column from the dataset_upload table. It then counts the number of records for each user. The WHERE clause limits the records to those uploaded starting from the first day of the last month up to the present. The HAVING clause limits the results to those users who have uploaded more than 100 datasets. The results are ordered by the count of uploads in descending order, so the user with the most uploads is at the top.
To solve a related customer analytics question on DataLemur's free online SQL coding environment, try this Microsoft Teams Power User SQL Interview Question:
SQL Question 2: Analyze Client Data Usage
LiveRamp, a leading provider of privacy-safe identity resolution, has a massive dataset of daily client data usage. For business sales strategies, the company wants you to analyze the usage data. The task is to write a SQL query to find out the daily average usage of each client for each product over the last 14 days from the current date, excluding weekends (Saturday and Sunday). Also, note that usage data is only recorded on weekdays when the clients use the product.
Consider the following tables:
Example Input:
| date | client_id | product_id | usage |
|---|---|---|---|
| 2022-10-03 | 1 | 100 | 1000 |
| 2022-10-03 | 1 | 200 | 1500 |
| 2022-10-04 | 2 | 100 | 2000 |
| 2022-10-04 | 1 | 200 | 1200 |
| 2022-10-05 | 2 | 100 | 2500 |
| 2022-10-05 | 1 | 200 | 1100 |
| 2022-10-06 | 3 | 100 | 1800 |
| 2022-10-06 | 1 | 200 | 900 |
| 2022-10-07 | 3 | 200 | 2000 |
| 2022-10-07 | 1 | 100 | 1500 |
| 2022-10-08 | 1 | 200 | 800 |
| 2022-10-09 | 2 | 100 | 3000 |
Answer:
To solve for this, we can use the function with a clause in PostgreSQL to calculate the average usage for each product over the last 14 days excluding weekends. The clause lets us define a window over which we compute a function, in this case, the 'average' over the "previous 14 days". Here is the SQL code:
This code will output a new table with columns: "date", "client_id", "product_id", and "avg_usage_14_days" where each row represents a day (from the most recent 14 days), a client, and a product, and the average usage of that product by that client over the last 14 days from the current date, excluding weekends.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: Can you explain the meaning of database denormalization?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Denormalization is done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.
For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.
SQL Question 4: Analyzing Customer Data Usage
LiveRamp provides data connectivity solutions, a crucial component of which is keeping track of how clients are using their platform. Your task, as a data scientist, is to design a database that captures the following information for each customer: how many times they log in per day, what features they use, and how long they use each feature. Specifically, we want to know what the most used feature is and the average time spent on it.
Here are some example tables to guide your data modeling:
Example Input:
| login_id | customer_id | login_date |
|---|---|---|
| 1 | 1001 | 08/08/2022 |
| 2 | 1002 | 10/08/2022 |
| 3 | 1001 | 09/08/2022 |
| 4 | 1002 | 11/08/2022 |
| 5 | 1003 | 12/08/2022 |
Example Input:
| usage_id | login_id | feature_name | usage_time_minutes |
|---|---|---|---|
| 1 | 1 | 'Feature A' | 25 |
| 2 | 2 | 'Feature B' | 30 |
| 3 | 1 | 'Feature B' | 20 |
| 4 | 3 | 'Feature A' | 40 |
| 5 | 2 | 'Feature A' | 10 |
| 6 | 2 | 'Feature B' | 15 |
| 7 | 4 | 'Feature B' | 45 |
Now, let's write a PostgreSQL query to determine the most used feature and the average time spent on it.
Answer:
This PostgreSQL query first groups the features by their name, and then counts how many times each feature is used and calculates the average usage time. The resulting data is then ordered by the count of times a feature is used in descending order. The clause at the end of the query restricts the output to the topmost record, which represents the most used feature and the average time a customer spends using this feature.
SQL Question 5: Can you describe the concept of a database index and the various types of indexes?
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes:
- unique & non-inuqie indexes
- primary & composite indexes
- clustered & non-clustered indexes
SQL Question 6: Filtering Customers Based on Multiple Conditions
LiveRamp connects customers with the brands they love by using data connectivity to understand customer preferences. For this task, imagine that we have a database table that lists all of LiveRamp's customers, including demographic and activity data.
As a Data Analyst, your task is to write a SQL query that can filter active customers in the city of "San Francisco" who are aged between 25 and 35, registered in the last 3 months, and their total point is higher than 500.
Example Input:
| customer_id | activity_status | city | age | registration_date | total_points |
|---|---|---|---|---|---|
| 785 | Active | San Francisco | 32 | 05/05/2022 | 750 |
| 894 | Active | New York | 28 | 06/21/2022 | 650 |
| 587 | Active | San Francisco | 37 | 06/15/2022 | 800 |
| 985 | Inactive | San Francisco | 26 | 04/15/2022 | 550 |
| 333 | Active | San Francisco | 30 | 05/12/2022 | 510 |
| 888 | Inactive | San Francisco | 28 | 06/22/2022 | 700 |
Answer:
This query filters the customers based on the required conditions. It uses the WHERE clause to specify the conditions, and the AND keyword to ensure that all conditions must be met. The BETWEEN keyword is used to filter the age, and the INTERVAL keyword is used to subtract 3 months from the current date for registration date comparison purpose. This query will return the list of customers that meet these criteria.
SQL Question 7: What's the purpose of the constraint?
A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.
The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.
For example, if you have a table of LiveRamp customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the LiveRamp customers table.
SQL Question 8: Computing Click-Through Rate for Ads
LiveRamp, a leading data connectivity platform, is keen on analyzing the effectiveness of their digital ads by observing their 'click-through rates'. In specific, the company wants to examine the number of times an advertisement was clicked on versus the number of times it was displayed.
You have two tables:
- : Contains information about the ads that have been displayed.
- : Contains information about which ads were clicked.
Example Input:
| ad_id | display_date | product_id |
|---|---|---|
| 101 | 06/08/2022 00:00:00 | 50001 |
| 102 | 06/10/2022 00:00:00 | 69852 |
| 103 | 06/18/2022 00:00:00 | 50001 |
| 104 | 07/26/2022 00:00:00 | 69852 |
| 105 | 07/05/2022 00:00:00 | 69852 |
Example Input:
| click_id | click_date | ad_id |
|---|---|---|
| 201 | 06/08/2022 00:02:00 | 101 |
| 202 | 06/10/2022 00:01:00 | 102 |
| 203 | 07/05/2022 00:05:00 | 105 |
Given these tables, write a SQL query to compute the click-through rates by product each month.
Answer:
The above SQL query groups the data by month and product id. Within each group, it computes the click-through rate by dividing the total number of unique clicks by the total number of ads displayed. The ensures that we consider all ads, even those that did not receive a click. To avoid division by zero or null value, the counts are converted to float before division.
To practice a similar problem about calculating rates, solve this TikTok SQL question on DataLemur's interactive coding environment:
SQL Question 9: What is the Average Monthly Revenue per Client?
LiveRamp, an identity resolution provider, plays a pivotal role in connecting various marketing platforms. Let's consider a simplified representation of the company's data which includes client id (), transaction date (), product id (), and the revenue associated with that transaction (). We are interested in the average monthly revenue per client.
Here is the sample data:
Example Input:
| trans_id | client_id | trans_date | product_id | revenue |
|---|---|---|---|---|
| 100 | 500 | 06/08/2022 | 101 | 5000 |
| 200 | 650 | 06/15/2022 | 102 | 1000 |
| 300 | 500 | 06/18/2022 | 103 | 4000 |
| 400 | 650 | 07/06/2022 | 101 | 3000 |
| 500 | 500 | 07/18/2022 | 102 | 2500 |
| 600 | 650 | 07/27/2022 | 103 | 1000 |
We would like to know the average monthly revenue per client.
Example Output:
| month | client_id | avg_revenue |
|---|---|---|
| 6 | 500 | 4500 |
| 6 | 650 | 1000 |
| 7 | 500 | 2500 |
| 7 | 650 | 2000 |
Answer:
This query performs a GROUP BY operation on the month (extracted from the transaction date) and client_id. It then calculates respectively, the average () revenue per client per month.
SQL Question 10: What are the similarities and differences between a clustered and non-clustered index?
Here is an example of a clustered index on the column of a table of LiveRamp customer transactions:
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 11: Filtering Customer Records With String Pattern
You are given the table which holds information about all customers of LiveRamp with the following schema:
table Example Input:
| customer_id | first_name | last_name | created_at | |
|---|---|---|---|---|
| 121 | John | Doe | john.doe@liveramp.com | 2018-03-14 |
| 422 | Jane | Doe | jane.doe@liveramp.com | 2018-05-19 |
| 323 | Martin | Smith | martin.smith@example.com | 2019-07-21 |
| 424 | Emma | Brown | emma.brown@liveramp.com | 2020-01-01 |
| 525 | Lucas | Green | lucas.green@example.com | 2020-02-02 |
To regularly send newsletters only to our more engaged customers whose email domain is 'liveramp.com', write a SQL query to get the list of customer_ids, first names, last names and email addresses of customers whose email addresses end with 'liveramp.com'.
Answer:
You can use the keyword to match a pattern in a string in SQL. To get only those customers whose email addresses end with 'liveramp.com', you can use the pattern '%liveramp.com', where '%' matches any sequence of characters.
This PostgreSQL query filters out the customers based on their email domains. The '%' symbol is a wildcard that matches any sequence of characters, and 'liveramp.com' is the specific string we want to match. Therefore, this query will return the list of LiveRamp customers.
Preparing For The LiveRamp SQL Interview
The key to acing a LiveRamp SQL interview is to practice, practice, and then practice some more!
Besides solving the earlier LiveRamp SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each problem on DataLemur has hints to guide you, full answers and best of all, there's an interactive coding environment so you can right in the browser run your SQL query and have it executed.
To prep for the LiveRamp SQL interview it is also helpful to solve interview questions from other tech companies like:
However, if your SQL skills are weak, forget about diving straight into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers things like GROUP BY and LEAD/LAG window functions – both of which come up frequently during SQL job interviews at LiveRamp.
LiveRamp Data Science Interview Tips
What Do LiveRamp Data Science Interviews Cover?
Besides SQL interview questions, the other question categories covered in the LiveRamp Data Science Interview are:
- Probability & Stats Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for LiveRamp Data Science Interviews?
The best way to prepare for LiveRamp Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Microsoft, Amazon & startups
- A Crash Course covering Stats, ML, & Data Case Studies
- Amazing Reviews (900+ 5-star reviews on Amazon)