Apache Spark Interview Questions Preparation Course

Apache Spark is one of the fastest growing trend in Data Science and Data engineering world. Big companies like Amazon, Netflix, Google etc use Apache Spark. This course is designed to help you achieve your goals in Data Science field. Data Engineer and Software Engineers with Apache Spark knowledge may get more salary than others with similar qualifications without Apache Spark knowledge.

In this course, you will learn how to handle interview questions on Apache Spark in Software Development. I will explain you the important concepts of Apache Spark.

You will also learn the benefits and use cases of Apache Spark in this course. 

What is the biggest benefit of this course to me?

Finally, the biggest benefit of this course is that you will be able to demand higher salary in your next job interview.

It is good to learn Apache Spark for theoretical benefits. But if you do not know how to handle interview questions on Apache Spark, you can not convert your Apache Spark knowledge into higher salary.

What are the topics covered in this course?

We cover a wide range of topics in this course. We have questions on Apache Spark, Spark architecture, tricky questions etc.

How will this course help me?

By attending this course, you do not have to spend time searching the Internet for Apache Spark interview questions. We have already compiled the list of most popular and latest Apache Spark Interview questions. 

Are there answers in this course?

Yes, in this course each question is followed by an answer. So you can save time in interview preparation.

What is the best way of viewing this course?

You have to just watch the course from beginning to end. Once you go through all the videos, try to answer the questions in your own words. Also mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this course 2-3 times, you will be well prepared to face a technical interview in Apache Spark field.

What is the level of questions in this course?

This course contains questions that are good for a Fresher to an Architect level. The difficulty level of question varies in the course from a Fresher to an Experienced professional.

What happens if Apache Spark concepts change in future?

From time to time, we keep adding more questions to this course. Our aim is to keep you always updated with the latest interview questions on Apache Spark.

What are the sample questions covered in this course?

Sample questions covered in this course are as follows:

1. What are the main features of Apache Spark?
2. What is a Resilient Distribution Dataset in Apache Spark?
3. What is a Transformation in Apache Spark?
4. What are security options in Apache Spark?
5. How will you monitor Apache Spark?
6. What are the main libraries of Apache Spark?
7. What are the main functions of Spark Core in Apache Spark?
8. How will you do memory tuning in Spark?
9. What are the two ways to create RDD in Spark?
10. What are the main operations that can be done on a RDD in Apache Spark?
11. What are the common Transformations in Apache Spark?
12. What are the common Actions in Apache Spark?
13. What is a Shuffle operation in Spark?
14. What are the operations that can cause a shuffle in Spark?
15. What is purpose of Spark SQL?
16. What is a DataFrame in Spark SQL?
17. What is a Parquet file in Spark?
18. What is the difference between Apache Spark and Apache Hadoop MapReduce?
19. What are the main languages supported by Apache Spark?
20. What are the file systems supported by Spark?
21. What is a Spark Driver?
22. What is an RDD Lineage?
23. What are the two main types of Vector in Spark?
24. What are the different deployment modes of Apache Spark?
25. What is lazy evaluation in Apache Spark?
26. What are the core components of a distributed application in Apache Spark?
27. What is the difference in cache() and persist() methods in Apache Spark?
28. How will you remove data from cache in Apache Spark?
29. What is the use of SparkContext in Apache Spark?
30. Do we need HDFS for running Spark application?
31. What is Spark Streaming?
32. How does Spark Streaming work internally?
33. What is a Pipeline in Apache Spark?
34. How does Pipeline work in Apache Spark?
35. What is the difference between Transformer and Estimator in Apache Spark?
36. What are the different types of Cluster Managers in Apache Spark?
37. How will you minimize data transfer while working with Apache Spark?
38. What is the main use of MLib in  Apache Spark?
39. What is the Checkpointing in  Apache Spark?
40. What is an Accumulator in Apache Spark?
41. What is a Broadcast variable in  Apache Spark?
42. What is Structured Streaming in  Apache Spark?
43. How will you pass functions to Apache Spark?
44. What is a Property Graph?
45. What is Neighborhood Aggregation in Spark?
46. What are different Persistence levels in Apache Spark?
47. How will you select the storage level in Apache Spark?
48. What are the options in Spark to create a Graph?
49. What are the basic Graph operators in Spark?
50. What is the partitioning approach used in GraphX of Apache Spark?