Databricks Interviews with 80 Real-World QnA with thedataengineerzone

Databricks & Spark Interview Questions — 80 Questions with Answers

Complete Data Engineering Interview Preparation Guide

Preparing for a Databricks, Spark, Azure Data Engineer, or Data Engineering interview?

This PDF contains 80 carefully structured interview questions with detailed visual explanations, practical examples, PySpark/SQL snippets, interview tips, common mistakes, and real-world scenarios.

From Databricks fundamentals to senior-level architecture and troubleshooting, this guide is designed to help you prepare systematically.

📚 What’s Inside?

🔹 Level 1 — Fundamentals

1. What is Databricks?

2. Explain Databricks architecture.

3. What is Databricks Runtime?

4. What are Databricks clusters?

5. How do you create and run a notebook?

6. How is Spark used in Databricks?

7. What is DBFS?

8. What are Databricks Jobs/Workflows?

9. What are the different cluster types?

10. What is a SQL Warehouse?

🔹 Level 2 — Data Engineering

11. How do you design a Databricks data pipeline?

12. Explain Bronze, Silver and Gold.

13. What is Delta Lake?

14. Delta Lake vs Parquet?

15. How do ACID transactions work in Delta Lake?

16. How do you implement incremental loading?

17. How do you handle schema evolution?

18. How do you handle duplicate records?

19. How do you implement CDC?

20. How do you handle bad records/data-quality failures?

🔹 Level 3 — Spark + Performance

21. What is partitioning?

22. What causes a shuffle?

23. How do you optimize a slow Spark job?

24. repartition() vs coalesce()

25. Broadcast join vs sort-merge join

26. What is data skew?

27. How do you solve data skew?

28. What is AQE?

29. Cache vs persist

30. How do you decide the number of shuffle partitions?

31. What is predicate pushdown?

32. What is partition pruning?

33. What is file compaction?

34. What is OPTIMIZE?

35. What is Photon?

🔹 Level 4 — Delta Lake

36. Explain Delta Lake architecture.

37. What is the Delta transaction log?

38. What is time travel?

39. VACUUM vs OPTIMIZE

40. What is MERGE INTO?

41. How do you perform upserts?

42. How do you implement SCD Type 1?

43. How do you implement SCD Type 2?

44. What happens when multiple records match during MERGE?

45. How do you recover from an incorrect Delta write?

🔹 Level 5 — Unity Catalog & Governance

46. What is Unity Catalog?

47. Unity Catalog vs Hive Metastore

48. Explain catalog → schema → table hierarchy.

49. What are managed vs external tables?

50. How do you implement row-level security?

51. How do you implement column-level security?

52. What is data lineage?

53. How do you manage permissions?

54. How do you securely access ADLS from Databricks?

55. What are external locations and storage credentials?

🔹 Level 6 — Streaming

56. What is Structured Streaming?

57. Batch vs streaming?

58. Explain checkpoints.

59. What is watermarking?

60. How do you handle late-arriving data?

61. What is output mode?

62. How do you achieve fault tolerance?

63. How do you process Kafka/Event Hub data?

64. How do you implement streaming → Delta?

65. How do you handle duplicate events?

🔹 Level 7 — Advanced / Senior-Level

66. How would you optimize a pipeline processing 10 TB/day?

67. A Databricks job suddenly became 3× slower. How would you troubleshoot it?

68. One Spark task takes much longer than the others. What could be the reason?

69. Your Delta table contains millions of small files. How would you fix it?

70. A join is causing an enormous shuffle. How would you optimize it?

71. Your pipeline fails halfway through. How would you make it restartable?

72. How would you design an idempotent Databricks pipeline?

73. How would you design a multi-layer production Lakehouse?

74. How would you implement CI/CD for Databricks?

75. How would you separate Dev, QA and Production?

76. How do you deploy notebooks/jobs without manually changing them?

77. How would you monitor production Databricks pipelines?

78. How would you control Databricks cost?

79. Databricks vs Snowflake — when would you choose each?

80. Design an end-to-end Azure Databricks data engineering architecture.

🎯 What Makes This Guide Different?

✅ 80 interview questions from beginner to senior level

✅ Visual handwritten-style explanations

✅ Real-world production scenarios

✅ PySpark & SQL examples

✅ Spark performance optimization

✅ Delta Lake concepts & practical scenarios

✅ Unity Catalog & data governance

✅ Structured Streaming

✅ Troubleshooting-based questions

✅ Architecture & system-design questions

✅ Interview tips and key takeaways

✅ Common mistakes to avoid

👨‍💻 Perfect For

Data Engineers • Azure Data Engineers • Databricks Engineers • Big Data Engineers • PySpark Developers • Cloud Data Engineers • Data Engineering Students • Interview Preparation

🔥 Stop memorizing random interview questions. Prepare with a structured 80-question Databricks interview guide covering the concepts and scenarios that matter in real interviews.

📘 80 Questions | Practical Examples | Visual Notes | Interview-Focused Preparation