Just finished one recently Databricks VO , the overall process consists of four rounds, basically maximizing engineering capabilities, algorithm capabilities, system design and communication capabilities. The overall question is not particularly tricky, but the follow-up is very deep. If you just stay in thinking about the questions, it is easy to continue to ask questions. Here we sort out the core content of each round as a reference for students preparing for Databricks interviews.
Algorithm Round
This round is an algorithm question, but it is not a typical LeetCode high-frequency routine question, but a simplified version of the Transportation Problem.
The question is given a variety of transportation methods, which can be understood as different transportation methods or different routes. A feasible path from the starting point to the ending point needs to be found and the overall transportation cost calculated. The basic version is actually more intuitive. As long as the problem is abstracted into a graph structure, the path can be found using BFS.
Coding Round
The question requires the implementation of a MxN TicTacToe class. The core method that needs to be implemented is Move(i,j), each time it is called, it means that the current player is in (i,j) Position placement.
After the method is executed, several operations need to be completed: updating the chessboard status, printing the current board, and determining whether the current player has won. The logic of the question itself is not complicated. The focus is on whether the class structure is clear, whether the state management is reasonable, and whether the player switching logic is correct.
After completing the basic version, the interviewer added a functional extension.
Add a parameter in constructor IsAI, used to control whether to enable AI battle mode. When IsAI = False When , the game logic remains normal two-player battle; when IsAI = True When using this method, it is necessary to automatically simulate the AI's positioning behavior.
Follow up
- If each transportation method has a different cost, how to calculate it? Minimum cost path
- How to design a data structure to record the cost of each path
- How to avoid double counting if there are many paths
- How does BFS scale to Cost-aware search
Behavioral Round
The third round is Behavioral Interview, basically a classic problem for North American technology companies.
The interviewer will dig deeper into the resume, including:
- Introduce one of the most challenging projects
- What are the technical difficulties encountered?
- How did you solve the problem
- How to deal with conflicts in the team
- Why you want to join Databricks
The overall rhythm is to dig into the project rather than talk about it in general.
It is recommended to use the STAR method to sort out several project cases when preparing:
- Technical challenges
- Decision making process
- Personal contribution
- Final result
This answer will be more logical.
System Design Round
The last round is system design, which is also the core round in the entire interview process.
Question background
The background of the question is very close to the Databricks machine learning platform scenario. The question given by the interviewer is to design a black box AutoML service, which requires explaining how this service works from the perspective of system architecture.
The design process needs to include the design of the API interface, the design of the database schema, and the data flow structure of the entire system. The description of the question itself is relatively long and contains many details. The interviewer is more concerned about whether the system can actually run. For example, how to schedule training tasks, how to store experimental results, how to manage model versions, and how to isolate different user tasks, etc. If you have experience in ML platform or data infrastructure, this round will be easier to start. If there is no relevant background, you may continue to be asked about system details.
What to do if you get stuck at the interview?
In fact, it’s not that many students don’t know how to do it, but that their ideas are stuck on the spot or they are not prepared to follow up.
For example: I can’t push through the algorithm question follow up, I don’t know how to expand on the system design question, and my thinking breaks down halfway through the coding question. In this case, if there is real-time thinking assistance, many questions can actually be solved on the spot. For example, some interview auxiliary services will provide remote real-time idea reminders and prompts at key nodes to help you continue to push forward the design or algorithm.
If you are also preparing for Databricks / FAANG / Quant / Big Tech interviews and need mock interviews or real-time thinking assistance, you can learn about: Programhelp Interview assistance service, familiar with the interview rhythm, many high-intensity VOs can actually be stabilized.
