10 Amplitude SQL Interview Questions (Updated 2024)
Data Science, Data Engineering and Data Analytics employees at Amplitude write ad-hoc SQL queries as a big part of their job. They use SQL for extracting and analyzing user behavior data from product analytics, and implementing data transformations for optimizing data models in the digital optimization industry. Because of this, Amplitude asks prospective hires SQL interview questions.
Thus, to help you prep for the Amplitude SQL interview, we've curated 10 Amplitude SQL interview questions in this blog.
10 Amplitude SQL Interview Questions
SQL Question 1: Analyzing User Event Activities in Amplitude
Imagine you're working at Amplitude as a data analyst, and you've been given a task to understand more about the users' behavior.
Your task is to write a SQL query to find the total number of unique active users () per day (), and compare each day's active user count with the previous day's count. Here we define active users as the users that have any events () on a certain day ().
The dataset () you have is as follows:
Example Input:
| event_id | user_id | event_date |
|---|---|---|
| 1 | 100 | 2022-09-01 |
| 2 | 200 | 2022-09-01 |
| 3 | 100 | 2022-09-01 |
| 4 | 300 | 2022-09-02 |
| 5 | 200 | 2022-09-02 |
| 6 | 300 | 2022-09-03 |
Answer:
This query first uses a common table expression ( clause) to create a temporary result set of the active users each day. In the next part, it uses the function, which is a window function that returns a value from a previous row (in this case, the previous day). Finally, it calculates the change in active user counts from the previous day using the result from the function. If there are no active users from the previous day, the function ensures that the result will be 0 instead of NULL.
For more window function practice, solve this Uber SQL Interview Question within DataLemur's interactive coding environment:
SQL Question 2: Employees Earning More Than Their Boss
Suppose you had a table of Amplitude employee salary data. Write a SQL query to find the employees who earn more than their own manager.
Amplitude Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns 8,000, surpassing her manager, William Davis who earns $7,800.
Try this problem interactively on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is tough, you can find a detailed solution here: Highly-Paid Employees.
SQL Question 3: How does a cross join differ from a natural join?
Imagine you are organizing a party and have two database tables: one table of people you want to invite and another list of food items you want to serve.
A cross join would be like inviting every person on your list to the party and serving them every food item on the menu, regardless of whether they like the food or not. So, if you had 10 people on your invite list and 5 food items on the menu, you would generate all 50 different combinations of people and food (10 x 5 = 50).
On the other hand, a natural join would be like inviting only the people who like the food items on the menu (based on doing a inner/left/right/outer JOIN on a common key like ).
SQL Question 4: Designing a product usage tracking Database for Amplitude
Amplitude is a Product Intelligence platform that helps teams build and design great product experiences. For this question, let's assume that Amplitude would like to track the behavior of users on its platform. Specifically, they would like to log every user session, the different features they interact with during that session, and the duration spent on each feature.
Design a database schema that would effectively capture this information, and write a SQL query to calculate the average time a user spends on each feature.
Let's theoretically have two tables: and .
Example Input:
| session_id | user_id | start_time | end_time |
|---|---|---|---|
| 101 | 1 | 06/08/2022 12:00:00 | 06/08/2022 12:35:00 |
| 102 | 1 | 06/09/2022 11:00:00 | 06/09/2022 11:25:00 |
| 103 | 2 | 06/09/2022 09:00:00 | 06/09/2022 09:15:00 |
| 104 | 3 | 07/26/2022 17:00:00 | 07/26/2022 17:10:00 |
| 105 | 1 | 07/05/2022 14:30:00 | 07/05/2022 15:00:00 |
Example Input:
| interaction_id | session_id | feature | start_time | end_time |
|---|---|---|---|---|
| 9001 | 101 | "Dashboards" | 06/08/2022 12:00:00 | 06/08/2022 12:05:00 |
| 9002 | 101 | "Reports" | 06/08/2022 12:05:00 | 06/08/2022 12:20:00 |
| 9003 | 102 | "Onboarding" | 06/09/2022 11:00:00 | 06/09/2022 11:10:00 |
| 9004 | 103 | "Dashboards" | 06/09/2022 09:00:00 | 06/09/2022 09:05:00 |
| 9005 | 104 | "Reports" | 07/26/2022 17:00:00 | 07/26/2022 17:07:00 |
| 9006 | 105 | "Dashboards" | 07/05/2022 14:30:00 | 07/05/2022 14:50:00 |
Answer:
This query first joins the table with the table based on the session id. Then, it calculates the average duration for each feature across all sessions by subtracting the start time from the end time for each interaction (converted to seconds using epoch), and averages this per feature.
SQL Question 5: Can you explain the distinction between a left and 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 Amplitude'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 6: Average Time Spent on Site Per User
At Amplitude, an analytics company, we want to keep track of how engaged our users are with our product. For this purpose, we keep a record of each user session and how long it lasts. For the purposes of analyzing our product's engagement, it will be useful for us to know the average length of time each user spends on our products. Can you write a SQL query that gets the average session duration per user?
Example Input:
| session_id | user_id | start_time | end_time |
|---|---|---|---|
| 45 | 500 | 2022-06-08 10:00:00 | 2022-06-08 10:30:00 |
| 78 | 500 | 2022-06-10 11:00:00 | 2022-06-10 11:45:00 |
| 129 | 255 | 2022-06-18 14:00:00 | 2022-06-18 15:00:00 |
| 135 | 500 | 2022-07-26 16:00:00 | 2022-07-26 16:33:00 |
| 256 | 255 | 2022-07-05 09:00:00 | 2022-07-05 09:22:00 |
Example Output:
| user_id | avg_session_length_minutes |
|---|---|
| 500 | 39.33 |
| 255 | 40.00 |
Answer:
This query calculates the average session length per user. It uses PostgreSQL's function to calculate the session length in seconds (since measures the number of seconds), then divides by 60 to find the session length in minutes. It returns a table with the and the corresponding average session length, in minutes, for that user.
To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for user's time spent on activities or this Twitter Tweets' Rolling Averages Question which is similar for user centered data analytics.
SQL Question 7: What does the function do, and when would you use it?
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 201 | un-subscribed | NULL |
| 301 | NULL | not_opted_in |
| 401 | not_active | very_active |
| 501 | very_active | very_active |
| 601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
| customer_id | email_engagement | sms_engagement |
|---|---|---|
| 101 | moderately_active | not_opted_in |
| 102 | un-subscribed | not_opted_in |
| 103 | not_active | not_opted_in |
| 104 | not_active | very_active |
| 105 | very_active | very_active |
| 106 | not_active | not_opted_in |
SQL Question 8: Filter Customer Data with SQL LIKE Keyword
Amplitude is interested in identifying customers whose last name starts with "Smi" in their customer database. You are required to write a SQL query that retrieves a list of customers whose last name matches this pattern. The list should be ordered alphabetically by last name.
Here is an example of client records for Amplitude:
Example Input:
| customer_id | first_name | last_name | join_date | |
|---|---|---|---|---|
| 1204 | John | Smith | johnsmith@example.com | 01/20/2020 |
| 2548 | Samantha | Johnson | sjohnson@example.com | 05/12/2019 |
| 3761 | Michael | Smiley | m.smiley@example.com | 06/30/2021 |
| 4923 | Alice | Smithers | alice_smithers@example.com | 12/15/2018 |
| 5109 | Bill | Smithe | bsmithe@example.com | 08/26/2020 |
Now, with this input, we need to get an output that lists customers with the last name starting with "Smi," arranged alphabetically by last name.
Example Output:
| customer_id | first_name | last_name | join_date | |
|---|---|---|---|---|
| 5109 | Bill | Smithe | bsmithe@example.com | 08/26/2020 |
| 4923 | Alice | Smithers | alice_smithers@example.com | 12/15/2018 |
| 1204 | John | Smith | johnsmith@example.com | 01/20/2020 |
Answer:
Here is the SQL query which can return the desired data from the database:
This query selects all the columns from our table where the starts with the string "Smi". The character is a wildcard that matches any sequence of characters in SQL. The clause then sorts the results in ascending order by .
SQL Question 9: Join Product and Review Data
You are working for the company Amplitude as a Data Analyst. The company is running an analysis to identify how each product is performing based on reviews provided by customers.
The company mainly has two tables - One for customers and their details, and the other for product reviews. The table has fields , , , and the table has fields , , , , .
Write a SQL query to get the customer details along with the total number of reviews and the average rating given by them.
Example Input:
| customer_id | first_name | last_name | email_id |
|---|---|---|---|
| 123 | John | Doe | johndoe@example.com |
| 265 | Jane | Smith | janesmith@example.com |
| 362 | Sara | Jones | sarajones@example.com |
Example Input:
| review_id | customer_id | product_id | review | rating |
|---|---|---|---|---|
| 6171 | 123 | 50001 | "Great product" | 4 |
| 7802 | 265 | 50001 | "Decent product" | 3 |
| 5293 | 123 | 69852 | "Could be better" | 3 |
| 6352 | 192 | 69852 | "Not good" | 2 |
| 4517 | 981 | 50001 | "Excellent product" | 5 |
Example Output:
| customer_id | first_name | last_name | email_id | total_reviews | avg_rating |
|---|---|---|---|---|---|
| 123 | John | Doe | johndoe@example.com | 2 | 3.5 |
| 265 | Jane | Smith | janesmith@example.com | 1 | 3 |
Answer:
The above query joins the table with the table using the field. Then it groups the results based on the and calculates the total number of reviews and average rating for each customer.
Because join questions come up routinely during SQL interviews, try this Snapchat JOIN SQL interview question:
SQL Question 10: Can you describe the meaning of database normalization in layman's terms?
To normalize a database, tables are divided into smaller, more specialized ones and relationships between them are defined via primary and foreign keys. This minimizes redundancy, making the database more flexible, scalable, and easier to maintain. Normalization also helps to ensure the accuracy of the data by reducing the likelihood of inconsistencies and errors.
Preparing For The Amplitude SQL Interview
The key to acing a Amplitude SQL interview is to practice, practice, and then practice some more!
Beyond just solving the earlier Amplitude SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each interview question has hints to guide you, step-by-step solutions and best of all, there is an interactive coding environment so you can right online code up your SQL query and have it checked.
To prep for the Amplitude SQL interview you can also be a great idea to solve SQL questions from other tech companies like:
But if your SQL coding skills are weak, forget about going right into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as joins and UNION vs. joins – both of which show up routinely during SQL interviews at Amplitude.
Amplitude Data Science Interview Tips
What Do Amplitude Data Science Interviews Cover?
Beyond writing SQL queries, the other types of problems to prepare for the Amplitude Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Data Case Study Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Amplitude Data Science Interviews?
You should deeply research the Amplitude product, and read their many blog posts on product analytics. You should also read the book Ace the Data Science Interview, because the book covers 201 interview questions sourced from Google, Microsoft & tech startups and has a crash course covering Stats, SQL & ML.