Configure Spark Partitioning in Distributed Data Processing, Including Shuffles and Partitions ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A Spark job reads a large dataset from HDFS and performs a map operation. The engineer notices that many tasks are reading data from remote nodes instead of local nodes, causing high network traffic. Which action would most likely improve data locality?

   • AUse coalesce to reduce the number of partitions.
   • BEnsure the cluster has enough executors to match the number of HDFS blocks.
   • CUse repartition to align partitions with HDFS block locations.
   • DIncrease the number of partitions to create smaller tasks.
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   What happens to data during a shuffle operation like groupByKey?

   • AData is written to disk and never read again
   • BData is moved across executors to group keys together
   • CData stays on the same executor and is processed locally
   • DData is replicated to all executors for fault tolerance
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4. 8foundation · easy

   What is the primary role of a partition in a Spark RDD or DataFrame?

   • ATo replicate data across multiple nodes for fault tolerance
   • BTo determine the unit of data that a single task processes in parallel
   • CTo store data in a columnar format for efficient compression
   • DTo define the schema and data types of the dataset
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5. 9foundation · easy

   What is data locality in Spark?

   • AThe process of compressing data to save space
   • BThe ability to store data in multiple formats
   • CThe tendency to process data on the node where it is stored to reduce network I/O
   • DThe practice of replicating data to all nodes for faster access
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6. 10expert · hard

   A Spark job joins two large DataFrames on a key that is not uniformly distributed. The engineer wants to reduce shuffle overhead while also improving data locality for subsequent range-based queries on the same key. Which partitioning strategy should the engineer use?

   • AUse coalesce on both DataFrames to reduce partitions.
   • BUse repartitionByRange on both DataFrames with the same number of partitions.
   • CUse broadcast join to avoid shuffling both DataFrames.
   • DUse repartition on both DataFrames with the same number of partitions.
   Reveal Solution Discussion Select an answer first