10 AppLovin SQL Interview Questions (Updated 2024)

Updated on

March 1, 2024

At AppLovin, SQL is used often for analyzing user engagement metrics in their Adjust product analytics service, and for optimizing personalized ad targeting strategies. That's why AppLovin almost always asks SQL coding questions in interviews for Data Science, Analytics, and & Data Engineering jobs.

As such, to help you study for the AppLovin SQL interview, we've collected 10 AppLovin SQL interview questions – can you solve them?

10 AppLovin SQL Interview Questions

SQL Question 1: Identify Power Users in AppLovin

AppLovin is a mobile advertising and app development company. Lets say, for this company "Power Users" are the users who have made more than 20 purchases in the past month. Your task is to write a SQL query to identify these power users.

Here are some sample tables associated with the app:

table stores user account information.

table stores user purchasing history.


Here is a PostgreSQL query to solve this problem:

This query first forms a subquery to identify users who have made more than 20 purchases in the last month. It groups the table by user_id and counts the number of purchases each user has made in the past month, returning only those user_ids that have made more than 20 purchases.

Then it joins this subquery with the table, using the user_id as a join key, effectively filtering the table to only include these "power users". It returns the user_ids and usernames of these power users.

To solve a related super-user data analysis question on DataLemur's free online SQL coding environment, try this Microsoft Teams Power User SQL Interview Question: Microsoft SQL Interview Question: Teams Super User

SQL Question 2: User Engagement Analysis

As an Analyst at AppLovin, you are tasked with assessing the user engagement of the various apps in terms of active users. Active users are considered users that open the app at least once a day. For this task, you are given a table named 'user_logs' that has the following columns:

  • : The identifier for each log entry
  • : The identifier for each user
  • : The identifier for each application
  • : The timestamp of when the user accessed the application

Write a SQL query that calculates the average daily active users (DAU) for each app in the last 30 days. Also, list the apps by the highest average DAU to the lowest.

Example Input:

1234534572022-06-08 14:30:00
1234678972022-06-08 15:00:00
1234734552022-06-08 16:00:00
1234845632022-06-08 12:30:00
1234934572022-07-08 14:30:00

Example Output:


Here count of distinct for each per day gives us daily active users. We then average this across the last 30 days to get the average daily active users.


This query first calculates the number of distinct users for each app every day in the last 30 days. Then it averages these daily active user counts for each app. Finally, the apps are ordered by average daily active users in descending order.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

SQL Interview Questions on DataLemur

SQL Question 3: How does and differ?

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 AppLovin:

This query retrieves the total salary for each Analytics department at AppLovin 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 AppLovin departments where the total salary is greater than $1 million

AppLovin SQL Interview Questions

SQL Question 4: Customers Filter on Active/Product Engagement

In the AppLovin business model, customer's engagement with different apps is important for business analytics and decision-making. Given two databases - customer's details and their app usage details - write a query to filter all customers from the USA, who have used the app more than 5 times in the last week, and have engagement time more than 100 minutes on the product ABC.

Here are the mock tables for the database records:

Example Input:
Example Input:


The following PostgreSQL query will help to solve the problem:

This query uses a JOIN clause to combine records from the Customers and AppUsage tables. The WHERE clause filters records for customers from the USA who interacted with the app 'ABC' more than 5 times in the last week and have more than 100 minutes of engagement time.

SQL Question 5: What's a constraint in SQL, and do you have any examples?

SQL constraints are used to specify rules for the data in a table. They can be applied to single or multiple fields in a table when the table is created, or after the table has been created using the ALTER TABLE command.

For example, say you had a database that stores ad campaign data from AppLovin's Google Analytics account.

Here's what some constraints could look like:

SQL Question 6: Average Duration Per User

As an analyst at AppLovin, you are tasked with analyzing user behavior for different mobile apps. Find the average duration (in minutes) a user spends on each app every day based on the data in the table.

Example Input:
137245610806/08/2022 00:00:0012
321545610806/08/2022 00:00:0024
387578920806/10/2022 00:00:0035
456145610806/10/2022 00:00:0012
896278920807/26/2022 00:00:0042


This query first groups the interactions by , , and the date of interaction. It then calculates the average duration per group. The function is used to extract date from the column to eliminate the time portion. This gives us the average amount of time each user spends on each app each day. The results are then ordered by user_id, app_id, and interaction date.

To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for calculating based on time spent on an app or this Twitter Histogram of Tweets Question which is similar for analyzing user behavior on a platform.

SQL Question 7: What does do in a SQL query?

The UNION operator combines the results from multiple SELECT statements into a single result set.

Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of AppLovin's Facebook ads and their Google ads:

This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.

Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.

SQL Question 8: Calculate the Click-Through-Rate for AppLovin Advertisement Campaigns

For AppLovin, a company known for its mobile games and mobile advertising, a common SQL problem might revolve around assessing the success of their advertisement campaigns.

A key metric in evaluating this is the click-through-rate (CTR), which is calculated by dividing the number of users who clicked on an ad by the number of total ad impressions.

Given two tables; one for and another for , calculate the click-through-rate for each campaign per day.

Example Input:
103410181706/01/2022 00:00:00
204310291806/01/2022 00:00:00
318210128906/02/2022 00:00:00
479110129206/02/2022 00:00:00
500210256206/02/2022 00:00:00
Example Input:
781210181706/01/2022 00:00:00
259310128906/02/2022 00:00:00
Example Output:


The PostgreSQL query to solve this problem might look like this:

The SQL query above calculates the click-through-rate (CTR) for each campaign and date. The CTR is calculated as the count of unique clicks by users divided by the count of unique ad impressions. We use a LEFT JOIN to merge data from the and table and GROUP BY and to get the CTR for each campaign and day.

To practice a similar problem about calculating rates, solve this SQL interview question from TikTok on DataLemur's interactive SQL code editor: Signup Activation Rate SQL Question

SQL Question 9: Filtering Customer Records for AppLovin

You're an SQL developer at AppLovin. The marketing team wants to target users from their database who have an email address from a certain email provider for a new campaign. Specifically, they wish to choose users with email addresses ending in "@gmail.com" and have subscribed to news.

Example Input:


Write a SQL query to retrieve all records of users who have a "@gmail.com" email address and have a subscribed status.


This query will return all records from the users table where the user's email address ends with "@gmail.com" and the subscription status is 'subscribed'. The "%" before "@gmail.com" in the LIKE clause acts as a wildcard, matching any character(s) that may come before "@gmail.com" in the email address.

SQL Question 10: What's the difference between window functions and ?

While both and are used to rank rows, the key difference is in how they deal with ties.

RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.

DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.

Suppose we had data on how many deals different salespeople at AppLovin closed, and wanted to rank the salespeople.

The result of this query would be:


As you can see, the function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the , gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.

AppLovin SQL Interview Tips

The key to acing a AppLovin SQL interview is to practice, practice, and then practice some more! In addition to solving the above AppLovin SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Google, Uber, and Microsoft. DataLemur Questions

Each SQL question has hints to guide you, step-by-step solutions and most importantly, there is an interactive coding environment so you can instantly run your query and have it graded.

To prep for the AppLovin SQL interview you can also be wise to solve SQL questions from other tech companies like:

In case your SQL foundations are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.

Interactive SQL tutorial

This tutorial covers SQL topics like GROUP BY and handling date/timestamp data – both of which come up often during AppLovin SQL assessments.

AppLovin Data Science Interview Tips

What Do AppLovin Data Science Interviews Cover?

Beyond writing SQL queries, the other types of questions to practice for the AppLovin Data Science Interview are:

AppLovin Data Scientist

How To Prepare for AppLovin Data Science Interviews?

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

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