Which of the following are the functions that Spark can provide? (Multiple choice)

Question

Options

• ADistributed memory computing engine
• BDistributed file system
• CUnified scheduling of cluster resources
• DStream processing functions

• A
  79% (44)
• B
  14% (8)
• C
  7% (4)

Explanation

A and D are correct because Spark's core identity is an in-memory distributed computing engine - it processes data across a cluster's RAM for speed - and it natively supports stream processing through Spark Streaming and Structured Streaming.

B is wrong because a distributed file system (like HDFS) is a storage layer; Spark is a compute engine that typically sits on top of HDFS, S3, or similar storage - it does not manage how files are stored or replicated across nodes.

C is wrong because unified cluster resource scheduling is the job of systems like YARN, Mesos, or Kubernetes; Spark can run on these schedulers but does not replace them.

Memory tip: Think of Spark as the "engine + processor" - it computes (A) and streams (D), but it does not store files (B) or schedule resources (C). Those belong to the infrastructure layer beneath it.

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