KPMG | Big Data Engineer Interview Questions

KPMG | Big Data Engineer Interview Questions

In this article, we will see the list of questions asked in KPMG India Company Interview for 2+ year of experience candidate in big data field.

Let’s see the Questions:

1. Can you give an overview of your experience working with PySpark and big data processing?
2. What motivated you to specialize in PySpark, and how have you applied it in your current role?

𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐁𝐚𝐬𝐢𝐜𝐬:
3. could you Explain the basic architecture of PySpark.
4. How does PySpark relate to Apache Spark, and what advantages does it offer in distributed system?

𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬:
5. Tell me the difference between a DataFrame and an RDD in PySpark.
6. Could you explain the transformations and actions in PySpark ?
7. Give an examples of PySpark DataFrame operations you frequently used.

𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐉𝐨𝐛𝐬:
8. How did you optimize the performance of PySpark jobs?
9. Explain the Different techniques for handling skewed data in PySpark?

𝐃𝐚𝐭𝐚 𝐒𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧:
10. Can you explain how data serialization works in PySpark.
11. Discuss the significance of choosing the right compression codec for your PySpark applications.

𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐃𝐚𝐭𝐚:
12. How do you deal with Null or missing values in PySpark DataFrames?
13. Are there any specific strategies or functions you will prefer for handling missing data?

𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐒𝐐𝐋:
14. Describe your experiences with PySpark SQL.
15. How do you execute SQL queries on PySpark DataFrames?

𝐁𝐫𝐨𝐚𝐝𝐜𝐚𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐒𝐩𝐚𝐫𝐤:
16. What is broadcasting, and how is it useful in PySpark jobs?
17. Give an example scenario where broadcasting can significantly improve performance.

𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:
18. Discuss your experience with PySpark’s MLlib.
19. Can you give an examples of ML algorithms you’ve implemented using PySpark?

𝐉𝐨𝐛 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠:
20. How do you monitor and troubleshoot the PySpark jobs?
21. Describe the importance of logging in PySpark jobs.

𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐎𝐭𝐡𝐞𝐫 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬:
22. Have you integrated PySpark with other big data technologies or databases? If so, please Give an examples.
23. How do you handle data transfer between PySpark and external systems?

𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨:
24. Explain the project that you worked on in your earlier/current organizations.
25. Describe a challenging PySpark project that you’ve worked on. What were the key challenges, and how did you overcome them?

𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭:
26. Explain your experience with cluster management in PySpark.
27. How do you scale PySpark applications in the cluster environment?

𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦:
28. Can you name and briefly describe some of the popular libraries or tools in the PySpark ecosystem, apart from the core PySpark functionality?

Reference: Linkedin Post

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Check out the given link for knowing about this company rating on Glassdoor: KPMG Glass Door

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