MCQs on Core Concepts | Apache Spark MCQs Questions

This comprehensive set of Apache Spark MCQs Question covers essential topics for mastering the core concepts of Spark. From understanding Resilient Distributed Datasets (RDDs) to exploring DataFrames and Datasets, these questions delve into transformations, actions, lazy evaluation, fault tolerance, and data partitioning. Ideal for anyone preparing for Apache Spark certifications or interviews, these questions test your knowledge of distributed computing fundamentals, enabling you to optimize data processing workflows effectively.

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Chapter 3: Core Concepts – MCQs

Topic 1: Understanding Resilient Distributed Datasets (RDDs)

1. What is an RDD in Apache Spark?
   a) A data structure for distributed data
   b) A streaming API
   c) A storage format for Spark
   d) A machine learning library
2. Which of the following is true about RDDs?
   a) They are immutable
   b) They allow lazy evaluation
   c) They support fault tolerance
   d) All of the above
3. How can you create an RDD in Spark?
   a) From a local collection
   b) From a file stored in HDFS
   c) By transforming an existing RDD
   d) All of the above
4. What happens when an RDD partition is lost?
   a) The entire RDD is recreated
   b) The partition is recomputed using lineage information
   c) The data is permanently lost
   d) A new RDD is created
5. Which operation cannot be performed directly on an RDD?
   a) Map
   b) Reduce
   c) SQL query
   d) Filter

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Topic 2: Transformations and Actions in RDDs

6. Which of the following is a transformation in Spark?
   a) Map
   b) Collect
   c) Count
   d) Take
7. What does the reduce action do in Spark?
   a) Combines the elements of an RDD
   b) Filters elements based on a condition
   c) Returns a new RDD with reduced size
   d) Creates a new partition
8. Which of the following is an action in Spark?
   a) Filter
   b) Map
   c) Take
   d) FlatMap
9. What is the primary difference between transformations and actions in Spark?
   a) Transformations compute results immediately, while actions are lazy
   b) Transformations are lazy, while actions compute results immediately
   c) Both are lazy operations
   d) Actions generate new RDDs
10. Which transformation can result in data shuffling across nodes?
    a) Filter
    b) GroupByKey
    c) Map
    d) FlatMap

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Topic 3: DataFrame and Dataset API Overview

11. What is a DataFrame in Apache Spark?
    a) A distributed collection of data organized into named columns
    b) A key-value pair data structure
    c) A machine learning library
    d) A function for RDD transformation
12. Which API offers the highest level of abstraction in Spark?
    a) RDD API
    b) DataFrame API
    c) Dataset API
    d) SQL API
13. Which of the following supports compile-time type safety in Spark?
    a) RDDs
    b) DataFrames
    c) Datasets
    d) SQL API
14. Which language is not directly supported by the DataFrame API?
    a) Python
    b) Java
    c) Scala
    d) C++
15. What is the key benefit of using DataFrame over RDD?
    a) More efficient memory usage
    b) Optimization through Catalyst optimizer
    c) Rich API for SQL-like queries
    d) All of the above

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Topic 4: Lazy Evaluation and Fault Tolerance

16. What does lazy evaluation mean in Spark?
    a) Operations are executed immediately
    b) Operations are executed only when an action is called
    c) All transformations are computed in parallel
    d) None of the above
17. Why is lazy evaluation beneficial in Spark?
    a) It reduces memory usage
    b) It allows Spark to optimize the execution plan
    c) It speeds up data transformations
    d) Both a and b
18. What ensures fault tolerance in Spark RDDs?
    a) Data replication across nodes
    b) Lineage information of RDDs
    c) Checkpointing
    d) Both b and c
19. What is the default behavior when an RDD computation fails?
    a) The entire application fails
    b) The lost partition is recomputed
    c) A new RDD is created manually
    d) The data is permanently lost
20. Which mechanism stores intermediate results to avoid recomputation in case of failure?
    a) Lineage
    b) Partitioning
    c) Checkpointing
    d) Serialization

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Topic 5: Partitions and Data Distribution

21. What is a partition in Spark?
    a) A subset of data processed in parallel
    b) A key-value pair
    c) A transformation operation
    d) A fault-tolerant mechanism
22. How can you control the number of partitions in Spark?
    a) Using the repartition function
    b) Using the coalesce function
    c) Both a and b
    d) Only through the Spark configuration
23. Which of the following can lead to uneven data distribution?
    a) Randomized partitioning
    b) Skewed data in key-based operations
    c) Using the default partitioning mechanism
    d) None of the above
24. Why is data partitioning important in Spark?
    a) To balance workload across nodes
    b) To optimize network usage
    c) To reduce shuffling during transformations
    d) All of the above
25. Which of the following operations can trigger data shuffling?
    a) Map
    b) ReduceByKey
    c) Filter
    d) Collect
26. What happens when a partition contains too much data?
    a) The node processing it may run out of memory
    b) It leads to data loss
    c) The partition is automatically split
    d) None of the above
27. Which Spark property allows you to specify the default number of partitions?
    a) spark.default.parallelism
    b) spark.executor.cores
    c) spark.rdd.partitions
    d) spark.memory.fraction
28. What is the recommended way to reduce the number of partitions?
    a) Using coalesce
    b) Using repartition
    c) Using reduceByKey
    d) Using aggregate
29. Which partitioning strategy is used by default for RDDs?
    a) Hash partitioning
    b) Range partitioning
    c) Round-robin partitioning
    d) Random partitioning
30. What is the purpose of partitioning in distributed systems like Spark?
    a) To ensure fault tolerance
    b) To enable parallel processing
    c) To optimize network usage
    d) All of the above

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Answer Key

Qno	Answer
1	a) A data structure for distributed data
2	d) All of the above
3	d) All of the above
4	b) The partition is recomputed using lineage information
5	c) SQL query
6	a) Map
7	a) Combines the elements of an RDD
8	c) Take
9	b) Transformations are lazy, while actions compute results immediately
10	b) GroupByKey
11	a) A distributed collection of data organized into named columns
12	b) DataFrame API
13	c) Datasets
14	d) C++
15	d) All of the above
16	b) Operations are executed only when an action is called
17	d) Both a and b
18	d) Both b and c
19	b) The lost partition is recomputed
20	c) Checkpointing
21	a) A subset of data processed in parallel
22	c) Both a and b
23	b) Skewed data in key-based operations
24	d) All of the above
25	b) ReduceByKey
26	a) The node processing it may run out of memory
27	a) spark.default.parallelism
28	a) Using coalesce
29	a) Hash partitioning
30	d) All of the above