Databricks DevOps / SRE Interview Questions

1.Tell me about a time you leveraged an open-source tool or contributed to an open-source project to solve a production infrastructure problem. What was the problem and how did you approach it?

2.Describe a situation where you had to admit you made a mistake in a production environment. How did you handle it with your team and stakeholders?

3.Describe a time when you saw a recurring infrastructure issue that wasn't technically your responsibility but you decided to fix it anyway. What did you do and what was the outcome?

4.Walk me through a time when you had to choose between using an existing proprietary tool versus adopting or contributing to an open-source alternative for your infrastructure. What factors did you consider?

5.Tell me about a time you had to implement a critical infrastructure change under significant time pressure while ensuring zero downtime. How did you maintain quality standards?

6.Tell me about the most significant infrastructure automation or tooling improvement you initiated and drove to completion without being asked to do it. What was your motivation and how did you get buy-in?

7.You're managing a Kubernetes cluster running Spark jobs that suddenly starts experiencing memory pressure and node failures. The jobs are processing Delta Lake tables for thousands of customers. How would you debug and resolve this?

8.Our CI/CD pipeline deploys Delta Lake and MLflow components across multiple cloud providers. A deployment just failed halfway through, leaving some regions updated and others on the old version. Walk me through your incident response.

9.You need to implement monitoring for Unity Catalog's data lineage tracking across our distributed lakehouse. The system needs to handle millions of table operations per hour while providing real-time lineage updates. What's your approach?

10.Design a system to automatically scale Photon Engine clusters based on query complexity and data volume, while maintaining sub-second response times for interactive analytics. Consider that queries can range from simple aggregations to complex ML feature engineering.

11.Our Delta Lake tables are experiencing frequent merge conflicts during concurrent writes from multiple Spark clusters. The conflicts are causing job failures and data inconsistencies. How would you architect a solution to handle high-concurrency writes while maintaining ACID guarantees?

12.You're tasked with building a global deployment system for MLflow model serving that needs to handle model updates, A/B testing, and rollbacks across thousands of endpoints while maintaining millisecond latencies. The system must support both real-time and batch inference workloads. Design the architecture.

13.Design a monitoring system for Unity Catalog that can track data access patterns across our entire lakehouse platform and alert on suspicious behavior within minutes. The system needs to handle 10 million data operations per hour across thousands of workspaces.

14.Our customers are running thousands of concurrent Spark jobs on the lakehouse platform, and job startup times have increased from 30 seconds to 5 minutes. How would you design a solution to get startup times back under 1 minute?

15.Design an autoscaling system for MLflow model serving that can handle traffic spikes from 100 requests per second to 50,000 RPS within 30 seconds, while maintaining sub-100ms P95 latencies. The models range from lightweight sklearn to large transformer models.

16.You need to build a disaster recovery system for our lakehouse that can restore customer data and metadata across Delta Lake, Unity Catalog, and MLflow within 4 hours of a complete region failure. How do you design this while minimizing ongoing costs?

17.Design a deployment system that can safely roll out Photon Engine updates to production clusters serving 50,000 concurrent queries without causing query failures or performance regressions. The system needs to support immediate rollback if issues are detected.

18.Build a cost attribution and optimization system that can track compute and storage costs across tens of thousands of Delta Lake tables and Spark jobs, then automatically recommend optimizations to reduce customer bills by 20-30% without impacting performance.

19.Tell me about a time you had to convince engineering teams to adopt a new infrastructure standard or practice when they were initially resistant. How did you approach it?

20.Describe a time when you identified that your infrastructure monitoring or alerting was missing something critical that affected customer workloads. How did you handle the immediate situation and prevent it from happening again?

21.Tell me about a time you had to lead a cross-functional effort to solve an infrastructure problem that was blocking multiple product teams. What was your strategy for coordinating the work?

22.Describe a situation where you had to push back on a product or engineering decision because of infrastructure constraints, but the team was under significant pressure to ship. How did you navigate that tension?

23.Tell me about the most senior engineering leader you've successfully influenced to change their mind about an infrastructure or technical decision. What was the situation and how did you approach it?

24.Describe a time when you had to make a significant infrastructure decision with incomplete information while other teams were depending on you. How did you approach the decision-making process and communicate with stakeholders?

25.Estimate how many Spark jobs are running simultaneously on Databricks' platform at peak hours. Walk me through your reasoning and assumptions.

26.Estimate how much compute cost Databricks could save customers annually if we reduced Spark job startup time from 45 seconds to 5 seconds. Walk through your analysis.

27.Our Unity Catalog metadata service is experiencing 50ms P95 latency, but customers expect sub-10ms for data discovery queries. How would you investigate and design a solution?

28.A Fortune 500 customer running 24/7 ML inference on MLflow serving just told us their model predictions are taking 2 seconds instead of 200ms. Walk me through your debugging approach.

29.Estimate the storage cost savings Databricks could achieve by implementing automatic Delta Lake table optimization across all customer workspaces. What assumptions drive your calculation?

30.You notice that Photon Engine query performance degrades by 30% when customer clusters autoscale from 2 to 20 nodes. Design an investigation and solution approach.