Explain the Apache Spark™ Architecture Execution Hierarchy.. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the correct execution flow in Apache Spark?

   • AAction → Stages → Job → Tasks
   • BTransformation → Job → Stages → Tasks
   • CAction → Job → Stages → Tasks
   • DJob → Action → Stages → Tasks
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2. 17foundation · easy

   Why can tasks within a stage be executed together?

   • ABecause they all process the same partition of data
   • BBecause they have narrow dependencies and do not require shuffling data between partitions
   • CBecause they are all triggered by the same action
   • DBecause they all run on the same executor
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4. 18foundation · easy

   What triggers the creation of a Spark job?

   • AA transformation such as map() or filter()
   • BAn action such as count() or saveAsTextFile()
   • CThe definition of a DataFrame schema
   • DThe creation of a SparkSession
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5. 19foundation · easy

   What happens when an action is called in Spark?

   • AThe transformations are added to the lineage graph but not executed
   • BThe scheduler creates a job and executes the required stages and tasks
   • CThe data is immediately written to disk
   • DThe SparkSession is restarted to run the job
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6. 20foundation · easy

   Which of the following is an example of a wide dependency in Spark?

   • Aunion()
   • BgroupByKey()
   • Cmap()
   • Dfilter()
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