Databricks Data Scientist Interview Questions

1.Tell me about a time you used or contributed to an open-source tool to solve a data science problem. What was the issue, what open-source solution did you choose, and what impact did it have?

2.Give me an example of when you identified a data quality or infrastructure issue that was affecting multiple teams, and you took ownership of coordinating the fix across those teams.

3.Describe a time when you noticed a gap in your team's data science workflow or tooling and took it upon yourself to fix it, even though it wasn't your assigned responsibility.

4.Walk me through a project where you had to deliver a high-quality model or analysis under an aggressive timeline. How did you balance speed and quality, and what was the outcome?

5.Tell me about a situation where being direct and honest about a data science problem or limitation put you at risk, but you chose transparency anyway. What happened?

6.Describe a time you extended or built upon an existing open-source machine learning framework to meet requirements it didn't originally support. What was missing and how did you approach the solution?

7.You're analyzing user engagement patterns in our Databricks workspace, and you notice that Spark jobs are frequently failing after running for hours. How would you approach diagnosing what's causing these failures at scale?

8.Write a Python function that efficiently computes rolling window statistics over a large time-series dataset in Spark, ensuring it can handle out-of-order data and late arrivals.

9.A customer wants to implement feature engineering pipelines that can serve both batch training and real-time inference. Their data lives across multiple cloud providers. How would you architect this using our platform?

10.You're building a recommendation model using customer transaction data in Delta Lake. The model needs to retrain daily but also support real-time inference. Walk me through your ML pipeline design.

11.A customer reports that their AutoML experiments in our platform are taking 6+ hours to complete, but they need results within 30 minutes for their business use case. How would you investigate and optimize this?

12.You need to implement a data quality monitoring system that can detect anomalies across thousands of Delta tables in real-time and automatically trigger downstream pipeline stops. How would you build this?

13.Our Unity Catalog needs to automatically classify and tag millions of columns across customer lakehouses to ensure sensitive data is properly governed. Design a system that can process this at scale while minimizing false positives.

14.Design a system that can detect when customer Delta Lake tables are becoming performance bottlenecks and automatically suggest or apply optimizations like partitioning, Z-ordering, or compaction.

15.You need to design a feature store that integrates with MLflow and can serve features to both batch training jobs and real-time inference endpoints across our platform. Walk me through your architecture.

16.Design a system that can automatically optimize Spark job configurations across thousands of customer workspaces based on their historical execution patterns and resource usage.

17.Our Mosaic AI platform needs to support training foundation models with billions of parameters across customer data that spans multiple clouds and regions. Design the training orchestration system.

18.You're designing a collaborative notebook system where data scientists across different teams can share live computation results and datasets while maintaining strict access controls through Unity Catalog. How do you handle this at enterprise scale?

19.Tell me about a time when you disagreed with your manager's prioritization of data science initiatives. How did you handle that situation?

20.Give me an example of when you identified a gap in your team's machine learning practices and drove adoption of a new approach or tool. How did you get everyone on board?

21.Describe a situation where you noticed a recurring data science issue affecting multiple customer workspaces, but solving it wasn't your direct responsibility. How did you approach it?

22.Walk me through a time when you had to onboard a new data scientist to your team while also delivering on a critical project deadline. How did you balance mentoring with execution?

23.Tell me about a time when you had to convince someone significantly more senior than you to change their approach to a data science problem. How did you build credibility with them?

24.Describe a situation where you had to coordinate data science work across multiple teams who had different technical approaches and timelines. What was your strategy?

25.Estimate how many Spark jobs are executed on the Databricks platform globally in a single day. Walk me through your assumptions and reasoning.

26.Estimate the storage cost savings Databricks customers achieve by using Delta Lake's automatic data optimization features. What assumptions would you make?

27.You notice that Delta Lake table reads are 30% slower this week compared to last week across customer workspaces. No new features shipped. How would you investigate this performance regression?

28.MLflow model serving latency increased by 200ms across all endpoints yesterday. Customer SLAs are at risk. How would you diagnose and prioritize fixes?

29.A Fortune 500 company wants to migrate from their existing data warehouse to Databricks, but they're concerned about cost. They currently spend $2M annually on their warehouse. Estimate what their Databricks bill might be and justify your reasoning.

30.Unity Catalog shows that 40% of customer data assets have no usage in the past 90 days. Design an approach to help customers identify and manage unused data, considering compliance and business requirements.