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DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK · Question #101

Which of the following identifies multiple narrow operations that are executed in sequence?

The correct answer is C. Stage. Stage correctly identifies multiple narrow operations grouped together and executed in sequence. In Apache Spark, a stage is a boundary-defined collection of narrow transformations (like map, filter, flatMap) that can be pipelined on each partition without triggering a shuffle…

Understanding Spark Job, Stage, and Task Execution Hierarchy

Question

Which of the following identifies multiple narrow operations that are executed in sequence?

Options

  • ASlot
  • BJob
  • CStage
  • DTask
  • EExecutor

How the community answered

(25 responses)
  • C
    88% (22)
  • D
    4% (1)
  • E
    8% (2)

Explanation

Stage correctly identifies multiple narrow operations grouped together and executed in sequence. In Apache Spark, a stage is a boundary-defined collection of narrow transformations (like map, filter, flatMap) that can be pipelined on each partition without triggering a shuffle across the cluster - stages run sequentially relative to one another, separated by wide (shuffle) operations.

Why the distractors are wrong:

  • A. Slot - a slot is a unit of resource allocation (CPU/memory) in a cluster, not a grouping of operations.
  • B. Job - a job is the highest-level unit triggered by an action (e.g., collect()); it contains multiple stages, not the other way around.
  • D. Task - a task is the smallest unit of work, running one operation on one partition; it lives inside a stage.
  • E. Executor - an executor is the JVM process that runs tasks; it's a compute resource, not an operational grouping.

Memory tip: Think of a stage like a theater stage - actors (tasks) perform a sequence of choreographed moves (narrow operations) without leaving the stage, until a scene change (shuffle) triggers the next stage.

Topics

#Spark Execution Model#Stages#DAG#Task Scheduling

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