Answer / Supriya Suman

"RDD Fault Tolerance" in Apache Spark ensures that data processing can continue even if one or more worker nodes fail. By default, Spark stores each RDD partition on multiple replicas across different nodes to ensure high availability. When a task fails, the system restores the data from replicas and recomputes the failed task.

What is spark deploy mode?

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What is serialization in spark?

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Can you run spark without hadoop?

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Explain first() operation in Apache Spark RDD?

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What file systems does spark support?

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How do we represent data in Spark?

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What is master node in spark?

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List the benefits of Spark over MapReduce.

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What is lineage graph in Apache Spark?

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List the languages supported by Apache Spark?

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Explain caching in spark streaming.

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What does map transformation do? Provide an example.

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