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Quiz on Transformation on RDD in Spark

Quiz on Transformation on RDD in Spark

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Question 1 of 10

1. Question

What is a transformation in Spark RDD?

An operation that stores data
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A function that immediately executes on data
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An operation that creates a new RDD from an existing one
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A command to stop a Spark job
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Question 2 of 10

2. Question

Which of the following is an example of a narrow transformation?

groupByKey()
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join()
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reduceByKey()
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map()
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Question 3 of 10

3. Question

What is the key feature of RDD transformations in Spark?

They run instantly when called
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They can modify the source RDD
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They are lazy and only executed when an action is called
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They require a database connection
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Question 4 of 10

4. Question

Which transformation returns multiple values for a single input?

map()
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flatMap()
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reduce()
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collect()
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Question 5 of 10

5. Question

Which transformation is typically used to remove duplicates from an RDD?

map()
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reduceByKey()
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union()
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distinct()
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Question 6 of 10

6. Question

What does the filter() transformation do

Changes the format of an RDD
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Removes elements that don’t satisfy a given condition
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Groups values by key
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Converts RDD to a DataFrame
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Question 7 of 10

7. Question

What is a wide transformation in Spark?

A transformation that changes RDD data type
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A transformation that can be executed without shuffling
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A transformation that causes data to move between partitions
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A transformation that increases memory size
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Question 8 of 10

8. Question

What does the union() transformation do in Spark?

Sorts the RDD
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Merges two RDDs
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Splits RDD into partitions
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Removes duplicates
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Question 9 of 10

9. Question

Which transformation groups all values having the same key into a list?

reduceByKey()
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groupByKey()
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flatMap()
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mapValues()
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Question 10 of 10

10. Question

Why should reduceByKey() be preferred over groupByKey() in most cases?

It is easier to write
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It avoids shuffling
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It uses memory instead of disk
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It performs local aggregation before shuffling, improving performance
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