11 Ambarella SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At Ambarella, SQL is used for managing and querying the overarching video processing database, and for analyzing data to improve visual recognition algorithms. Because of this, Ambarella almost always asks SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.

To help you ace the Ambarella SQL interview, we'll cover 11 Ambarella SQL interview questions – able to answer them all?

11 Ambarella SQL Interview Questions

SQL Question 1: Identifying Top Active Customers in Ambarella

Ambarella is a company specialized in ultra-high resolution and high dynamic range video compression hardware. They want to identify the power users, i.e., the customers who frequently purchase their products. Your task is to write a SQL query to find the top 5 customers who made the highest number of purchases in the last 6 months (consider current date as '2022-08-31') from the customer and purchase tables.

Example Input:
Example Input:


This SQL query will use an INNER JOIN to combine the and tables on the column. It then only considers purchases within the last 6 months by using the WHERE clause . It will then group the results by user and count how many purchases each customer has made. Finally, it orders the results by in descending order and limit the output to top 5 records.

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

SQL Question 2: Average Monthly Sales Revenue

Assume that Ambarella, a high-definition video technology company, is interested in tracking the average monthly sales revenue for each product it sells.

Suppose we have a table that records every sale made, including which was sold, the , and the . Your task is to write a SQL query that calculates the average monthly revenue for each product.

Example Input:
Example Output:


The query utilizes the aggregate function to calculate the average price (which is revenue in our case since it's a per-unit price) per month for each product. The function is used to group the sales by months. The output is then ordered by month and then product for easier readability.

For more window function practice, solve this Uber SQL Interview Question within DataLemur's online SQL coding environment:

Uber Data Science SQL Interview Question

SQL Question 3: How does the constraint function, and in what scenarios might it be useful?

The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.

For example, say you had Ambarella customer data. You could use a CHECK constraint to ensure that the email column contains only properly formatted email addresses, or that the age column contains only positive integer. Here's an example of that:

Ambarella SQL Interview Questions

SQL Question 4: Calculate the Average Salary of Ambarella Employees by Department

At Ambarella, an artificial intelligence (AI) vision silicon company, the Human Resources Department would like to know about the average salary of employees in different departments for budget planning. Write a SQL query to find the average salary for each department.

Please use the tables below to structure your solution:

Example Input:
Example Input:
Example Output:


This query first joins the and tables on the column. Then, it groups the data by , and for each department, calculates the average salary. The result is a table with department ids and their corresponding average salaries.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for grouping and aggregating product data or this Facebook Teams Power Users Question which is similar for calculating the top contributors.

SQL Question 5: Can you explain what SQL constraints are, and why they are useful?

Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.

Say you had a table of Ambarella employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:

NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.

UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.

PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.

FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.

CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that salary values are always positive numbers.

DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the employee hire date to the current date if no value is provided when a new employee is added to the database.

SQL Question 6: Calculate the Click-Through-Rate (CTR) for Ambarella's Products

Ambarella, a tech products company, frequently places digital ads for its multiple products. Each time an ad is viewed, the action is logged. If a viewer clicks on the ad, that is also logged.

You have two tables, and . The table logs each time an ad is viewed, and the table logs each time an ad is clicked.

Example Input:
100106/08/2022 00:00:00101
100206/10/2022 00:00:00102
100306/18/2022 00:00:00101
100407/26/2022 00:00:00102
100507/05/2022 00:00:00102
Example Input:
100106/08/2022 00:05:00101
100206/10/2022 00:10:00102
100407/26/2022 00:30:00102

The objective is to write a PostgreSQL query that will calculate the Click-Through-Rate (CTR) for each product as:
( Number of Clicks / Number of Views ) * 100


This query works by first joining the and tables together based on the , so that it's possible to compare views and clicks for each specific ad.

The clause groups the results by , so we get a separate CTR for each product. The CTR calculation is done by taking the count of from the and tables respectively. As multiple views or clicks from the same ad_id will be treated as one, keyword was used to eliminate the duplication. Finally, this is multiplied by 100 to get the percentage figure. The result is the CTR for each product.

Please notice that a decimal cast is needed as Postgres' division between two integers result in a rounded off integer, which would highly distort our result.

To practice a related SQL interview question on DataLemur's free online SQL code editor, solve this Meta SQL interview question: Meta SQL interview question

SQL Question 7: Can you define what a database index is, and give some examples of different 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 8: Calculate Average Sale Price Per Product Type

You have been given two tables, and . The table contains records of each sale, with the selling price and the product ID. The table contains a list of every product, with its product ID and product type. Your task is to write a query that calculates the average sale price for each product type and groups the result by product type.

Example table:

Example table:

Example Output:


This query first joins the and table on , so for each sale, we know what product was sold. Then we use the statement to gather all sales based on product type. The function is then applied to each group of sales to calculate the average sale price for each product type.

SQL Question 9: Finding Specific Customer Records

As an SQL specialist at Ambarella Corporation, your task is to pinpoint customers whose first names begin with the letter 'A' and also live in the state of California. We want to offer these customers a special promotional deal. Provide a SQL query that extracts these customer records.

Example Input:

1032ArthurHolmes1234 Elm StCalifornia
5687AliceJohnson5678 Pine StNew York
2954BarbaraSmith7890 Oak StCalifornia
8420AmyMiller2468 Spruce StCalifornia
3769AndrewDavis1357 Cedar StTexas

Example Output:

1032ArthurHolmes1234 Elm StCalifornia
8420AmyMiller2468 Spruce StCalifornia


In the query above, we use the keyword in conjunction with a wildcard (%) to catch all customer first names that start with the letter 'A'. The keyword is used to add the condition that the customer's state must be 'California'. This query will return all customers who meet these conditions.

SQL Question 10: Describe the difference between UNION and UNION ALL.

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at Ambarella working on a Marketing Analytics project. If you needed to get the combined result set of both Ambarella's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

SQL Question 11: Analyzing Customer and Sales Data for Ambarella

Given two tables, and , write a SQL query that analyzes the customer purchasing behavior. Specifically, find out the total sales revenue from each city where the customers are located.

The table has the following structure:

Example Input:

Each row in the table represents a transaction detailing the customer's ID (), the product they bought (), and the sales price of the product ().

The table is formatted as:

Example Input:
101JohnDoeSan Francisco
102JaneDoeLos Angeles
103JoeSmithSan Francisco
105JamesBrownLos Angeles

Each row in the table represents a customer, including their ID (), first name (), last name (), and the city where they live ().


This query joins the and tables on the field, then groups the combined table by the city of the customer. It sums the sales price of all sales transactions in each city, leading to the total sales revenue per city.

Since joins come up frequently during SQL interviews, take a stab at this SQL join question from Spotify: Spotify JOIN SQL question

Ambarella SQL Interview Tips

The best way to prepare for a Ambarella SQL interview is to practice, practice, practice. In addition to solving the earlier Ambarella SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Microsoft and Silicon Valley startups. DataLemur SQL and Data Science Interview Questions

Each problem on DataLemur has multiple hints, detailed solutions and best of all, there is an online SQL coding environment so you can instantly run your SQL query and have it graded.

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

But if your SQL skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.

SQL tutorial for Data Analytics

This tutorial covers things like window functions and filtering on multiple conditions using AND/OR/NOT – both of these pop up frequently in Ambarella interviews.

Ambarella Data Science Interview Tips

What Do Ambarella Data Science Interviews Cover?

Beyond writing SQL queries, the other topics covered in the Ambarella Data Science Interview are:

  • Probability & Statistics Questions
  • Coding Questions in Python or R
  • Business Sense and Product-Sense Questions
  • ML Modelling Questions
  • Behavioral Interview Questions

Ambarella Data Scientist

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