Common Data Engineer Theoretical Interview Questions

Common Data Engineer Theoretical Interview Questions

In this post, we will see the common Data Engineer Theoretical Interview Questions asked in Companies.

Let’ s see the questions:

Basic Level Interview Questions:

  1. Explain Hadoop Architecture?
  2. What is 5 v’s of big data?
  3. What is default number of replica in Hadoop? Can you increase or decrease it?
  4. Difference between Hadoop (Gen1) and Hadoop (Gen2)?
  5. What is heartbeats in Hadoop? why is that important?
  6. Write down few Linux commands?
  7. What is partition, shuffling, sort in MapReduce?
  8. What is Record Reader?
  9. Explain Sqoop Eval Command?
  10. Explain different optimizations used in Sqoop?
  11. Explain Combiner in MapReduce?
  12. What is Yarn? Why is it used?
  13. Features of Sqoop? Explain significance of them?
  14. Explain Boundary Val’s Query? Explain the formula?
  15. Explain different Modes available in Sqoop that used in job execution?
  16. Difference between Target Vs Warehouse directory in Hive?
  17. What is split by command in Sqoop? when it is used?
  18. Explain Hive Architecture?
  19. Explain Transactional Processing Vs Analytical Processing?
  20. Difference between Hive and RDBMS?
  21. What is seek time in Hive?
  22. Difference between SQL Vs HQL?
  23. Explain UDF  concept in hive? How many types?
  24. What is views in hive?
  25. Explain Managed Table and External Table?
  26. Explain Spark Architecture?
  27. What is transformations and actions? Give some example of it.

Intermediate Level Interview Questions :

  1. Explain different no. of optimizations in hive?
  2. Explain types of Joins?
  3. What is Map side Join? What is Bucket Map Join and Sort Merge Bucket join(SMB)?
  4. Explain SCD Types in Hive?
  5. Explain File-formats in hive?
  6. Explain CAP Theorem?
  7. Explain RDD? Difference between RDD Vs Dataframe vs Dataset?
  8. Define Broadcast in Spark?
  9. Explain Catalyst optimizer?
  10. Difference between Client Mode vs Cluster Mode?
  11. Explain Cache & Persist in Spark?
  12. Explain Spark Performance Optimizations?
  13. Explain Spark Accumulators?

This article takes reference from given LinkedIn post:Post

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