75 real Spark Core questions from the Big Data bank, as asked in Indian campus drives and tech interviews. Every question has a verified answer and an AI-tutor explanation on placd — free to start.
1. What is Spark action?
Junior
A.an operation that triggers execution and returns a result (collect, count, save)
B.a shared write-only variable in Spark that workers add to and the driver reads, used for counters and metrics
C.a set of tasks that can run without a shuffle, bounded by shuffle (wide) dependencies in the DAG
D.a transformation needing no shuffle, where one input partition maps to one output partition
A.Spark action — the Spark process that runs the main function, creates the SparkContext, builds the DAG, and coordinates tasks across executors
B.Spark action — an operation that triggers execution and returns a result (collect, count, save)
C.Spark action — a long-running auxiliary process that serves shuffle blocks so executors can be removed under dynamic allocation without losing shuffle data
D.Spark action — the basic unit of parallelism in Spark, a logical chunk of a dataset that one task processes
A.Spark transformation — the Spark process that runs the main function, creates the SparkContext, builds the DAG, and coordinates tasks across executors
B.Spark transformation — a lazy operation that returns a new RDD/DataFrame without executing (map, filter, join)
C.Spark transformation — memory Spark manages outside the JVM heap (spark.memory.offHeap) to reduce garbage-collection overhead for execution and storage
D.Spark transformation — a transformation requiring a shuffle, which creates a stage boundary
A.stage boundary — a lazy operation that returns a new RDD/DataFrame without executing (map, filter, join)
B.stage boundary — the point created by a shuffle (wide dependency) that splits a job into stages
C.stage boundary — a long-running auxiliary process that serves shuffle blocks so executors can be removed under dynamic allocation without losing shuffle data
D.stage boundary — a shared write-only variable in Spark that workers add to and the driver reads, used for counters and metrics
A.coalesce() — an operation that triggers execution and returns a result (collect, count, save)
B.coalesce() — memory Spark manages outside the JVM heap (spark.memory.offHeap) to reduce garbage-collection overhead for execution and storage
C.coalesce() — a long-running auxiliary process that serves shuffle blocks so executors can be removed under dynamic allocation without losing shuffle data
D.coalesce() — reduces the number of partitions without a full shuffle
A.Spark driver — the smallest unit of execution in Spark, operating on a single partition of data within a stage
B.Spark driver — a long-running auxiliary process that serves shuffle blocks so executors can be removed under dynamic allocation without losing shuffle data
C.Spark driver — the model where execution and storage share one region and can borrow each other's space, with execution able to evict cached blocks
D.Spark driver — the process that builds the DAG and schedules tasks
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