Shared Variables and Broadcast Joins ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the main benefit of using a broadcast join in Spark?

   • AIt allows the join to be performed without any network communication
   • BIt avoids shuffling the larger table by broadcasting the smaller table to all executors
   • CIt reduces the memory usage on each executor by storing only a portion of the data
   • DIt increases the parallelism of the join by splitting both tables into more partitions
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2. 17application · medium

   A team uses an accumulator to count the number of null values in a column. They notice that the accumulator value is higher than expected when the job runs with speculative execution enabled. What is the most likely cause?

   • ASpeculative execution can run duplicate tasks, causing accumulator updates to be applied more than once.
   • BThe accumulator is not thread-safe.
   • CThe accumulator is being reset by the driver.
   • DThe accumulator is not registered with the SparkContext.
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4. 18foundation · easy

   Which characteristic describes an accumulator in Spark?

   • AIt is mutable and can be read and written by tasks
   • BIt is automatically persisted to disk for fault tolerance
   • CIt is read-only and shared across all executors
   • DIt is write-only from the perspective of tasks
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5. 19application · medium

   A developer creates an accumulator named 'errorCount' and increments it inside a map transformation. After calling an action, they print the accumulator value on the driver. What is the most likely outcome?

   • AThe accumulator may not reflect all updates due to lazy evaluation and task retries.
   • BThe accumulator will always show the exact count of errors.
   • CThe accumulator will be reset to zero after the action.
   • DThe accumulator will throw an exception because it is used inside a transformation.
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6. 20foundation · easy

   What is the primary purpose of an accumulator in Spark?

   • ATo provide a mutable shared state that tasks can read and write
   • BTo share a large read-only dataset across executors
   • CTo aggregate information from tasks back to the driver
   • DTo cache intermediate results for faster recomputation
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