Databricks System Design interview questions focus on designing large-scale data platforms that support batch and streaming ETL, interactive analytics, and machine learning workflows. Expect problems that reward clear requirement gathering, simple MVP architectures that evolve to petabyte scale, and explicit trade-off discussions—latency versus throughput, cost versus performance, and consistency versus availability. Interviewers typically evaluate structured problem solving, cloud-native design choices (object storage, compute autoscaling, Delta Lake concepts), operational concerns like monitoring and recovery, and your ability to communicate architecture and trade-offs concisely. For interview preparation, practice end-to-end designs for pipelines, query engines, and model-training orchestration; sketch diagrams quickly; and rehearse explaining scaling strategies, partitioning, caching, and fault tolerance decisions. Timebox mock interviews to sharpen how you clarify goals, propose an MVP, then iterate to address nonfunctional requirements and failure modes. Finally, prepare concrete examples from your experience that show ownership and trade-off reasoning—these make your designs believable and practically grounded.