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A specialist in the big data market should be able to do and code statistical and quantitative analyses. A solid understanding of mathematics and logical reasoning are also required. 

A big data professional should be knowledgeable about various algorithms and data sorting techniques.

If you are preparing for a role in Big Data, then it'd be wise to list out the different technical and behavioral skills important for a role in this domain.

Big data MCQ

1.What are the classic three Vs of big data?

2.What is HDFS?

3.What is the core idea of the MapReduce programming model?

4.What is the main advantage of Apache Spark over classic MapReduce?

5.What is an RDD in Spark?

6.What is the difference between a transformation and an action in Spark?

7.Why is a Spark DataFrame usually preferred over an RDD?

8.What is a shuffle in a distributed processing engine?

9.What is data skew in a distributed job?

10.What is a broadcast join used for?

11.What does the CAP theorem state?

12.What does eventual consistency mean?

13.What is Apache Kafka primarily used for?

14.What determines ordering guarantees in Kafka?

15.What is the difference between batch and stream processing?

16.What is a watermark in stream processing?

17.What is the lambda architecture?

18.Why is Parquet commonly used for analytical data?

19.What do table formats such as Delta Lake and Apache Iceberg add to object storage?

20.What is partitioning in a big data table?

21.What is the small files problem in HDFS or object storage?

22.What is YARN's role in a Hadoop cluster?

23.What is Apache Hive?

24.What kind of workload suits a NoSQL key-value store such as Cassandra?

25.What is denormalisation in a big data context?

26.What does idempotency mean for a data pipeline?

27.What does exactly-once processing require in a streaming system?

28.Why is schema evolution important in big data systems?

29.What is data lineage in a big data platform?

30.What is the main purpose of a data catalogue?

31.What is the difference between a data lake and a data warehouse?

32.What is the primary reason to compute aggregates in advance?

33.What is a bloom filter used for in big data storage?

34.What is the significance of data locality in distributed processing?

35.What is a common cause of a Spark job failing with out of memory errors?

36.Why should collect() be used cautiously in Spark?

37.What does horizontal scaling mean?

38.What is the purpose of checkpointing in a streaming application?

39.What is the most common practical bottleneck when scaling a big data pipeline?

40.Why is data quality testing essential in a big data pipeline?

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