Databricks Interview Questions

Databricks is an innovative data and AI technology firm founded by the original creators of Apache Spark, Delta Lake, and MLflow. The company’s mission is to radically simplify the complex lifecycle of data (from ingestion, ETL, BI, to machine learning and AI applications) through a unified platform backed by next-generation query engines and structured storage systems.

As an industry leader, Databricks focuses on creating a new architectural pattern, the Lakehouse, which aims to replace traditional data warehouse architecture. Through a multi-year vision with incremental deliverables, this open platform seeks to unify data warehousing and advanced analytics.

In this article, we’ll review Databricks’ interview process and some practice questions to help you prepare.

While the structure of Databricks’ interview process follows the industry standard format, the company heavily emphasizes cultural fit throughout all of the stages. Here’s a quick breakdown of the process:

1. Initial Screen

   This involves a brief meeting or call with a recruiter or hiring manager. Here, the company will gauge your interest in the role and share more details about the position and culture. Candidates will generally be asked about their professional background, technical expertise, and why they want to work for Databricks.

2. Technical Screen

   Databricks places a strong emphasis on technical proficiency. This stage of the process is a combination of coding challenges, data analysis tasks, and discussions about past projects. Be prepared to delve deep into your past work experiences, especially those relating to Big Data, machine learning, or cloud computing.

3. Onsite Interview

   The onsite interview at Databricks is a comprehensive assessment of a candidate’s fit for the role. It’ll consist of multiple rounds with different team members, including data scientists, engineers, and product managers. Interview topics can include more in-depth technical assessments, problem-solving exercises, and behavioral questions to assess cultural fit.

Quick Tips for Databricks Interviews

• Know the product: Databricks is known for its Unified Analytics Platform. Familiarize yourself with its features, capabilities, and how it stands out in the Big Data and AI landscape.
• Showcase your expertise: Databricks values deep technical knowledge. Be ready to discuss complex data problems you’ve tackled, your methodologies, and the impact of your solutions.
• Emphasize company goals: Databricks wants to simplify Big Data processing, freeing data teams to focus on data, not the infrastructure. Highlight experiences or perspectives that resonate with this mission.

Databricks’ operations heavily rely on SQL for data management. Candidates are expected to have a strong grasp on writing queries and pulling data.

1. Find the sum of the purchases for each product in an e-commerce store.

You’re working with the sales team of an e-commerce store to analyze their monthly performance.

They give you the sales table that tracks every purchase made in the store. The table contains the columns id (purchase id), product_id, date (purchase date), and price.

Write a SQL query to compute the cumulative sum of sales for each product, sorted by product_id and date.

Note: The cumulative sum for a product on a given date is the sum of the price of all purchases of the product that happened on that date and all previous dates.

1. Find how much was spent on each item by Costco shoppers who registered in 2022.

You work at Costco, which has a database with two tables: a users table with user information and a purchases table with item purchase history.

Write a query to get the total amount spent on each item in the purchases table by users that registered in 2022.

1. Write a query to get monthly user activity and sales reports for one year.

Given a transactions , products, and users table, write a query to show the number of users, number of transactions placed, and total order amount per month in 2020. Assume that we are only interested in the monthly reports for a single year (January-December).

To prepare more for Database interview questions, try the SQL learning path and the full list of SQL questions in our database.

Databricks’ operations, especially with Apache Spark, greatly depend on coding and algorithmic solutions. As the company continues to revolutionize Big Data analytics and processing, candidates are expected to have a deep understanding of these topics.

1. How would you find if the characters of two strings have a one-to-one correspondence?

Given two strings, string1 and string2, write a function str_map to determine if a one-to-one correspondence (bijection) exists between the characters of string1and string2.

Note: For the two strings, our correspondence must be between characters in the same position/index.

1. Design three classes given specific functionalities.

You need to create three classes: text_editor, moving_text_editor, and smart_text_editor.

Each class has specific methods like write_line, delete_line, special_operation, and get_notes. The moving_text_editor and smart_text_editor classes extend the text_editor class and override the special_operation method.

1. Determine the indices of two integers that sum to a target integer.

You are given an array and a target integer. Your task is to write a function sum_pair_indices that returns the indices of two integers in the array that add up to the target integer. If not found, return an empty list.

To continue practicing Coding and Algorithms interview questions, try using the Python learning path or the full list of Coding and Algorithms questions in our database.

Statistics serve as the foundation for the data-driven insights that guide Databricks’ business decisions. Questions you may be asked include:

1. Determine if a coin is fair based on the outcome of 10 flips.

You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if this coin is fair.

1. What’s the probability of receiving a certain number of impressions on a platform?

On a naive advertising platform with an audience of size A and a limited amount of impressions B, each impression goes to one user at random. Every user has the same chance of receiving each impression.

Calculate the probability that a user receives exactly 0 impressions and that every person receives at least 1 impression.

1. Determine if a two-sided coin is biased.

You have a two-sided coin that could be fair or biased. Design a test and describe the outcome that would indicate the coin is biased.

To master Statistics and Probability topics, try out the Statistics and A/B testing and the Probability learning paths for comprehensive lesson guides and questions.

Machine learning is not just a buzzword for Databricks– it’s central to their innovations in data processing and analytics. With the company’s commitment to simplifying machine learning and AI applications in the data lifecycle, candidates are often assessed on their ability to handle real-world machine learning challenges.

1. How would you assess the accuracy and validity of a model?

Assume you’ve built a V1 of a spam classifier for emails. Describe what metrics you would use to evaluate the model’s accuracy and validity.

1. How would you build a model to bid on new keywords given a dataset of keywords and their prices?

You’re working on keyword bidding optimization and have a dataset with two columns: one for keywords being bid against and another with the price paid for those keywords. How would you construct a model to bid on a new, unseen keyword?

1. Design a fraud detection model for a bank.

You work at a bank that wants to build a model to detect fraud on the platform. The bank also wants to implement a text messaging service that will alert customers when the model detects a fraudulent transaction, allowing the customer to approve or deny the transaction with a text response.

How would you build this model, and what considerations would you take into account?

To prepare for machine learning interview questions, we recommend taking the machine learning course.