In this article, we will see 150 Apache Spark MCQs with Answer for Interviews and Exams. Apache Spark is an open-source, distributed computing system that offers a fast and general-purpose cluster-computing framework. Originally developed at the University of California, Berkeley's AMPLab, Spark was later donated to the Apache Software Foundation, where it has become one of its most active projects. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Here we are going to look into 150 MCQs with answer on various topics including Spark's core concepts, architecture, RDDs, DataFrames, Datasets, Spark SQL, Spark Streaming, MLlib, GraphX, cluster management, performance optimization, and best practices.

 

150 Apache Spark MCQs with Answer for Interviews and Exams

Also Read: 110 Grafana MCQs with answers for all Interviews and Exams

1. What is Apache Spark primarily used for?

a) Web development
b) Data processing
c) Mobile app development
d) 3D modeling

Ans: b) Data processing

 

2. Which language is not natively supported by Apache Spark?

a) Python
b) Scala
c) Java
d) PHP

Ans: d) PHP

 

3. What is an RDD in Apache Spark?

a) Real-time Data Deployment
b) Resilient Distributed Dataset
c) Rapid Data Development
d) Relational Data Drive

Ans: b) Resilient Distributed Dataset

 

4. Which feature of Apache Spark contributes to its high processing speed?

a) Disk-based storage
b) In-memory computation
c) Single-threaded processing
d) Batch processing

Ans: b) In-memory computation

 

5. Apache Spark Streaming is used for:

a) Batch processing
b) Real-time data processing
c) Static data analysis
d) Persistent data storage

Ans: b) Real-time data processing

 

6. Which of the following is a component of Apache Spark?

a) Spark SQL
b) Hadoop MapReduce
c) Apache Flume
d) Kafka Streams

Ans: a) Spark SQL

 

7. What does the SparkContext class do in Apache Spark?

a) Manages Spark job workflows
b) Provides connectivity to a Spark cluster
c) Stores data in a distributed manner
d) Processes real-time data

Ans: b) Provides connectivity to a Spark cluster

 

8. What is the function of an Action in Spark?

a) To modify data in RDD
b) To store data in RDD
c) To trigger a computation in RDD
d) To create a new RDD

Ans: c) To trigger a computation in RDD

 

9. Which file format is commonly used in Spark for big data processing?

a) CSV
b) JSON
c) Parquet
d) XML

Ans: c) Parquet

 

10. What is a DataFrame in Spark?

a) A special type of RDD for tabular data
b) A machine learning model
c) A visualization tool
d) A type of database connection

Ans: a) A special type of RDD for tabular data

 

11. In Apache Spark, which operation would you use to transform an RDD without triggering computation?

a) Action
b) Transformation
c) Broadcast
d) Accumulator

Ans: b) Transformation

 

12. Which of the following is true about Spark's lazy evaluation?

a) It immediately computes results when an action is called.
b) It postpones the computation until an action is called.
c) It only works with Spark Streaming.
d) It reduces the need for memory resources.

Ans: b) It postpones the computation until an action is called.

 

 

13. In Spark, what does the term 'lineage' refer to?

a) The history of data transformations.
b) The process of data replication.
c) The sequence of nodes in a cluster.
d) The algorithm used for data sorting.

Ans: a) The history of data transformations.

 

14. Which library in Apache Spark is used for machine learning?

a) Spark SQL
b) Spark Streaming
c) MLlib
d) GraphX

Ans: c) MLlib

 

15. What is the role of a Driver in Spark's architecture?

a) It manages the Spark Context.
b) It stores the data in a distributed manner.
c) It runs the main() function of an application.
d) It directly communicates with the database.

Ans: c) It runs the main() function of an application.

 

16. Which of these is not a feature of Apache Spark?

a) Real-time processing
b) In-memory computation
c) Automatic garbage collection
d) Fault tolerance

Ans: c) Automatic garbage collection

 

17. What is the primary purpose of the SparkConf object in Apache Spark?

a) To configure Spark properties and cluster parameters
b) To connect to a database
c) To create RDDs
d) To perform data analysis

Ans: a) To configure Spark properties and cluster parameters

 

18. In Apache Spark, which method is used to filter data in an RDD?

a) map()
b) reduce()
c) filter()
d) collect()

Ans: c) filter()

 

19. Which of the following operations is an action in Apache Spark?

a) map()
b) flatMap()
c) count()
d) filter()

Ans: c) count()

 

20. How does Apache Spark achieve high efficiency for complex iterative algorithms?

a) By using disk-based storage
b) By executing iterative algorithms in a single step
c) By caching intermediate results in memory
d) By distributing data evenly across the cluster

Ans: c) By caching intermediate results in memory

 

21. Which command in Spark SQL is used for registering a DataFrame as a table?

a) createOrReplaceTempView()
b) registerDataFrameAsTable()
c) createDataFrame()
d) registerTempTable()

Ans: a) createOrReplaceTempView()

 

22. In Apache Spark, persisting an RDD using the MEMORY_AND_DISK_SER storage level means:

a) The RDD is stored only in memory.
b) The RDD is stored only on disk.
c) The RDD is stored in memory first, and then on disk if it does not fit in memory.
d) The RDD is replicated across multiple nodes.

Ans: c) The RDD is stored in memory first, and then on disk if it does not fit in memory.

 

23. What does a SparkSession in Apache Spark enable?

a) Connection to the Spark cluster
b) Real-time data streaming
c) Access to Spark SQL, DataFrame, and DataSet APIs
d) Graph processing capabilities

Ans: c) Access to Spark SQL, DataFrame, and DataSet APIs

 

24. In Apache Spark, which one of the following is true about the 'reduceByKey' operation?

a) It is used to sort the dataset by key.
b) It aggregates values of each key using a specified function.
c) It filters out records based on keys.
d) It splits the dataset into multiple smaller datasets.

Ans: b) It aggregates values of each key using a specified function.

 

25. Which of the following is not a component of the Apache Spark ecosystem?

a) Spark SQL
b) Hadoop YARN
c) MLlib
d) GraphX

Ans: b) Hadoop YARN

 

26. What does the 'collect()' action in Apache Spark do?

a) It retrieves the entire RDD data to the driver node.
b) It distributes data across the worker nodes.
c) It saves the RDD data to an external storage system.
d) It aggregates the data in the RDD using a specified function.

Ans: a) It retrieves the entire RDD data to the driver node.

 

27. Which of the following best describes a 'narrow transformation' in Spark?

a) A transformation that causes data shuffling across partitions.
b) A transformation that does not require data movement across partitions.
c) A transformation that reduces the number of RDD partitions.
d) A transformation that can only be applied to small datasets.

Ans: b) A transformation that does not require data movement across partitions.

 

28. In Spark, what is the primary benefit of using DataFrames over RDDs?

a) DataFrames are faster as they are based on in-memory computation.
b) DataFrames provide more APIs than RDDs.
c) DataFrames support custom memory management.
d) DataFrames allow for optimizations through Spark's Catalyst optimizer.

Ans: d) DataFrames allow for optimizations through Spark's Catalyst optimizer.

 

29. How does Apache Spark achieve fault tolerance?

a) Through data replication on multiple nodes.
b) By restarting failed tasks on different nodes.
c) Through lineage information to rebuild lost data.
d) By storing data in a fault-tolerant file system like HDFS.

Ans: c) Through lineage information to rebuild lost data.

 

30. What is the main difference between 'transformations' and 'actions' in Apache Spark?

a) Transformations are lazy, while actions are eager.
b) Transformations operate on DataFrames, while actions operate on RDDs.
c) Transformations modify the data, while actions return a value to the driver program.
d) Transformations can only be executed on a cluster, while actions can be executed locally.

Ans: a) Transformations are lazy, while actions are eager.

 

31. Which of the following best describes the purpose of Spark's MLlib?

a) To enhance the real-time processing capabilities of Spark.
b) To provide machine learning libraries for scalable and efficient processing.
c) To provide libraries for graph computation and analysis.
d) To manage and deploy Spark applications across various cluster managers.

Ans: b) To provide machine learning libraries for scalable and efficient processing.

 

32. In Spark, what does the 'flatMap()' transformation do?

a) It merges multiple RDDs into a single RDD.
b) It applies a function to each element and flattens the result.
c) It filters elements based on a specified condition.
d) It maps each input record to a key-value pair.

Ans: b) It applies a function to each element and flattens the result.

 

33. What is the role of the DAG Scheduler in Apache Spark?

a) It schedules the execution of stages based on RDD dependencies.
b) It manages the allocation of resources across the cluster.
c) It ensures fault tolerance by replicating data.
d) It schedules tasks for real-time processing.

Ans: a) It schedules the execution of stages based on RDD dependencies.

 

34. Which statement is true about Spark's 'saveAsTextFile' action?

a) It saves the RDD contents as a single text file.
b) It saves each RDD partition as a separate text file in a directory.
c) It converts RDDs into DataFrames before saving.
d) It is used to save streaming data into text files.

Ans: b) It saves each RDD partition as a separate text file in a directory.

 

35. In Apache Spark, which operation would result in a shuffle?

a) map()
b) filter()
c) reduceByKey()
d) mapPartitions()

Ans: c) reduceByKey()

 

36. Which API in Apache Spark is best suited for dealing with structured data?

a) RDD API
b) DataFrame API
c) Dataset API
d) Broadcast variables

Ans: b) DataFrame API

 

37. What is the primary function of the 'groupByKey()' transformation in Spark?

a) It sorts the dataset based on the key.
b) It aggregates values for each key in the dataset.
c) It groups values with the same key.
d) It filters the dataset by keys.

Ans: c) It groups values with the same key.

 

38. In Spark, what is the significance of partitioning in RDDs?

a) It determines the schema of the RDD.
b) It defines how data is distributed across the cluster.
c) It specifies the number of replicas for fault tolerance.
d) It sets the number of tasks for each job.

Ans: b) It defines how data is distributed across the cluster.

 

39. Which of the following is a characteristic of Spark's 'lazy evaluation'?

a) It enhances fault tolerance.
b) It reduces the number of read-write operations to disk.
c) It immediately computes and stores the results of transformations.
d) It executes transformations as soon as they are defined.

Ans: b) It reduces the number of read-write operations to disk.

 

40. In Apache Spark, what does 'broadcast variable' refer to?

a) A variable that is distributed and used across multiple nodes in a cluster.
b) A variable that holds the configuration settings for a Spark application.
c) A mutable variable that accumulates updates.
d) A variable used for debugging purposes in a distributed environment.

Ans: a) A variable that is distributed and used across multiple nodes in a cluster.

 

41. Which component in Apache Spark is responsible for job execution and task scheduling?

a) SparkContext
b) DAG Scheduler
c) Cluster Manager
d) Task Scheduler

Ans: b) DAG Scheduler

 

42. In Apache Spark, what is a 'stage' in the context of task execution?

a) A collection of tasks that can be executed in parallel
b) A single task within a job
c) A phase where data is read from external storage
d) The process of writing results back to storage

Ans: a) A collection of tasks that can be executed in parallel

 

43. What does the 'cache()' method do in Apache Spark?

a) It saves the RDD to disk
b) It persists the RDD in memory for faster access
c) It clears the cached data in RDD
d) It replicates the RDD across multiple nodes

Ans: b) It persists the RDD in memory for faster access

 

44. In Spark, what is the primary difference between 'reduce()' and 'fold()' actions?

a) 'reduce()' can be used with non-associative operations, while 'fold()' cannot
b) 'fold()' requires an initial value, while 'reduce()' does not
c) 'fold()' operates on each partition, while 'reduce()' operates on the entire RDD
d) 'reduce()' is an action, while 'fold()' is a transformation

Ans: b) 'fold()' requires an initial value, while 'reduce()' does not

 

45. Which Spark library provides APIs for graph processing and computation?

a) Spark SQL
b) MLlib
c) GraphX
d) Spark Streaming

Ans: c) GraphX

 

46. What is the main advantage of using Broadcast variables in Apache Spark?

a) They allow sharing a large dataset across all the nodes in the cluster efficiently.
b) They enable the storage of data in disk.
c) They are used for aggregating logs.
d) They facilitate real-time data processing.

Ans: a) They allow sharing a large dataset across all the nodes in the cluster efficiently.

 

47. Which of the following operations will result in a wide transformation in Spark?

a) map()
b) flatMap()
c) groupBy()
d) mapPartitions()

Ans: c) groupBy()

 

48. In Apache Spark, what does the term 'lineage graph' represent?

a) A visualization of the cluster's node distribution.
b) The sequence of operations applied to form an RDD.
c) The hierarchical structure of DataFrames.
d) The network topology of the Spark cluster.

Ans: b) The sequence of operations applied to form an RDD.

 

49. Which of the following is true about the 'mapPartitions()' transformation in Spark?

a) It applies a function to each partition of the RDD.
b) It applies a function to each element of the RDD.
c) It rearranges the data in the RDD based on a key.
d) It reduces the number of partitions in an RDD.

Ans: a) It applies a function to each partition of the RDD.

 

50. In Apache Spark, what is the role of a Worker Node?

a) It is responsible for storing data and executing the tasks.
b) It coordinates the tasks and schedules jobs.
c) It is used to distribute the application code.
d) It acts as an interface between the user and the Spark application.

Ans: a) It is responsible for storing data and executing the tasks.

 

51. In Apache Spark, what does the 'accumulator' do?

a) It broadcasts a read-only variable to the worker nodes.
b) It stores intermediate results of transformations.
c) It provides a way to aggregate data across the cluster.
d) It manages the distribution of tasks across the cluster.

Ans: c) It provides a way to aggregate data across the cluster.

 

52. What is the default level of parallelism in Apache Spark?

a) The number of cores on the machine running Spark.
b) The number of partitions of the largest RDD.
c) The total number of cores available in the cluster.
d) The number of worker nodes in the cluster.

Ans: c) The total number of cores available in the cluster.

 

53. In Spark, what is the significance of the 'checkpointing' feature?

a) It enhances the performance of transformations.
b) It saves the final output of the application.
c) It provides fault tolerance by saving the state of an RDD.
d) It replicates RDDs to multiple nodes for reliability.

Ans: c) It provides fault tolerance by saving the state of an RDD.

 

54. Which of the following best describes Spark's 'Structured Streaming'?

a) A framework for real-time data processing.
b) A method for structuring unstructured data.
c) A tool for batch processing of structured data.
d) A technique for static data analysis.

Ans: a) A framework for real-time data processing.

 

55. What is the main benefit of Spark's in-memory processing?

a) It ensures data durability.
b) It reduces the need for disk space.
c) It provides increased processing speed.
d) It simplifies the programming model.

Ans: c) It provides increased processing speed.

 

56. Which Spark operation is used to combine two datasets with a common key?

a) union()
b) join()
c) combine()
d) merge()

Ans: b) join()

 

57. In Apache Spark, what is the role of the Cluster Manager?

a) It manages the database connections.
b) It schedules and allocates resources for applications.
c) It stores and processes data.
d) It executes the application code.

Ans: b) It schedules and allocates resources for applications.

 

58. What type of processing does Apache Spark primarily support?

a) Batch processing
b) Stream processing
c) Both batch and stream processing
d) Real-time processing only

Ans: c) Both batch and stream processing

 

59. Which feature in Spark allows for the processing of data larger than the memory size?

a) Disk persistence
b) Dynamic allocation
c) Broadcast variables
d) In-memory computation

Ans: a) Disk persistence

 

60. In Spark, what is the purpose of the 'partitionBy()' method?

a) It merges multiple RDDs based on a condition.
b) It repartitions an RDD based on a key.
c) It sorts the elements of an RDD.
d) It filters an RDD based on a partitioning scheme.

Ans: b) It repartitions an RDD based on a key.

 

61. Which of the following is not a valid level of data persistence in Apache Spark?

a) MEMORY_ONLY
b) MEMORY_AND_DISK
c) DISK_ONLY
d) CPU_ONLY

Ans: d) CPU_ONLY

 

62. In Apache Spark, what does the 'coalesce()' method do?

a) It combines multiple datasets into one.
b) It repartitions an RDD to a specified number of partitions.
c) It decreases the number of partitions in an RDD.
d) It increases the number of partitions in an RDD.

Ans: c) It decreases the number of partitions in an RDD.

 

63. What is the purpose of the 'SparkConf' object in a Spark application?

a) It is used to read data from external sources.
b) It sets up the initial configuration for a Spark application.
c) It manages the distributed data storage.
d) It schedules and runs Spark jobs.

Ans: b) It sets up the initial configuration for a Spark application.

 

64. Which of the following is a correct way to define a custom accumulator in Apache Spark?

a) Extending the AccumulatorV2 class
b) Using the 'createAccumulator()' method
c) Implementing the Accumulator interface
d) Calling the 'newAccumulator()' function

Ans: a) Extending the AccumulatorV2 class

 

65. What is the primary benefit of the Catalyst optimizer in Spark SQL?

a) It automatically manages memory usage.
b) It enhances the execution speed of SQL queries.
c) It provides a user-friendly interface for writing SQL queries.
d) It allows integration with external databases.

Ans: b) It enhances the execution speed of SQL queries.

 

66. Which Spark component is responsible for optimizing logical plans into physical execution plans?

a) SparkContext
b) Catalyst Optimizer
c) DAG Scheduler
d) Task Scheduler

Ans: b) Catalyst Optimizer

 

67. In Spark, what does the 'aggregate()' action do?

a) It filters elements of the RDD based on a condition.
b) It sorts the elements of the RDD.
c) It computes a summary value over the RDD.
d) It splits the RDD into multiple smaller RDDs.

Ans: c) It computes a summary value over the RDD.

 

68. Which of the following is not a function of Spark's Cluster Manager?

a) Managing the allocation of resources to applications
b) Storing data in memory or on disk
c) Monitoring the health of worker nodes
d) Launching executors on worker nodes

Ans: b) Storing data in memory or on disk

 

69. In Apache Spark, what is the purpose of the 'filter()' transformation?

a) To merge two RDDs based on a condition
b) To apply a function to each element of the RDD and return a new RDD
c) To select elements of the RDD that meet a specified condition
d) To reduce the number of elements in the RDD

Ans: c) To select elements of the RDD that meet a specified condition

 

70. What is the primary characteristic of an 'Action' in Spark's programming model?

a) It alters the structure of an RDD.
b) It triggers the execution of transformations and returns a result.
c) It describes how an RDD is computed from other RDDs.
d) It is used to configure properties of the Spark application.

Ans: b) It triggers the execution of transformations and returns a result.

 

71. What does the 'saveAsNewAPIHadoopFile()' action in Apache Spark do?

a) It saves the RDD in a file format compatible with Hadoop.
b) It creates a new Hadoop cluster.
c) It reads data from a Hadoop file.
d) It converts an RDD to a Hadoop dataset.

Ans: a) It saves the RDD in a file format compatible with Hadoop.

 

72. In Spark, what is the main purpose of the 'foreach()' action?

a) To iterate over each element of the RDD and apply a function.
b) To return a new RDD after applying a function to each element.
c) To filter out elements based on a condition.
d) To aggregate elements using a specified function.

Ans: a) To iterate over each element of the RDD and apply a function.

 

73. Which of the following best describes the 'repartition()' method in Spark?

a) It reduces the number of partitions in an RDD.
b) It increases the number of partitions in an RDD.
c) It sorts the data within each RDD partition.
d) It merges two RDDs into one.

Ans: b) It increases the number of partitions in an RDD.

 

74. In Apache Spark, which of the following is a benefit of using DataFrames over RDDs?

a) DataFrames are more flexible in terms of data manipulation.
b) DataFrames provide better performance due to Spark's Catalyst optimizer.
c) DataFrames support more programming languages than RDDs.
d) DataFrames are the only way to interact with Spark Streaming.

Ans: b) DataFrames provide better performance due to Spark's Catalyst optimizer.

 

75. What is the primary function of the 'take()' action in Apache Spark?

a) It collects all the elements of the RDD to the driver node.
b) It takes the first n elements from the RDD and returns them to the driver node.
c) It takes a random sample of elements from the RDD.
d) It partitions the RDD into n partitions.

Ans: b) It takes the first n elements from the RDD and returns them to the driver node.

 

76. What is the main purpose of Partitions in Apache Spark?

a) To enable parallel processing of data.
b) To store data on disk.
c) To manage the cluster's resources.
d) To serialize data for network transfer.

Ans: a) To enable parallel processing of data.

 

77. Which Spark library provides a unified, high-level API for batch and stream data processing?

a) Spark SQL
b) MLlib
c) GraphX
d) Structured Streaming

Ans: d) Structured Streaming

 

78. In Apache Spark, what is a 'job'?

a) A single task executed on a worker node.
b) A collection of stages triggered by an action.
c) A transformation applied to an RDD.
d) A Spark application submitted to the cluster.

Ans: b) A collection of stages triggered by an action.

 

79. Which of the following is not a valid level of data persistence in Apache Spark?

a) MEMORY_ONLY
b) DISK_ONLY
c) MEMORY_AND_DISK_SER
d) DISK_AND_NETWORK

Ans: d) DISK_AND_NETWORK

 

80. In Apache Spark, what does the 'reduceByKey()' transformation do?

a) It merges the values for each key using a specified reduce function.
b) It filters the elements of the RDD based on a key.
c) It creates a new RDD by applying a function to each key-value pair.
d) It groups data by key across multiple nodes.

Ans: a) It merges the values for each key using a specified reduce function.

 

81. Which feature of Apache Spark helps in optimizing the execution plans of queries?

a) Lazy Evaluation
b) Catalyst Query Optimizer
c) RDD (Resilient Distributed Dataset)
d) Spark Streaming

Ans: b) Catalyst Query Optimizer

 

82. What is an RDD in the context of Apache Spark?

a) Real-time Data Deployment
b) Rapid Data Development
c) Resilient Distributed Dataset
d) Relational Database Design

Ans: c) Resilient Distributed Dataset

 

83. In Spark, which method is used to aggregate all the elements of the RDD?

a) collect()
b) reduce()
c) aggregate()
d) fold()

Ans: b) reduce()

 

84. Which Apache Spark component is responsible for distributing and scheduling applications across the cluster?

a) Spark Core
b) Cluster Manager
c) DAG Scheduler
d) Task Scheduler

Ans: b) Cluster Manager

 

85. In Apache Spark, what is the purpose of a 'lineage graph'?

a) To visualize the data distribution across nodes
b) To track the sequence of transformations applied to an RDD
c) To represent the dependency graph of tasks
d) To monitor the performance of Spark applications

Ans: b) To track the sequence of transformations applied to an RDD

 

86. Which of the following best describes a DataFrame in Apache Spark?

a) A low-level abstraction for distributed data processing.
b) A distributed collection of data organized into named columns.
c) A type of external storage system supported by Spark.
d) A tool for graph processing.

Ans: b) A distributed collection of data organized into named columns.

 

87. In Apache Spark, what is the primary purpose of the 'SparkSession' object?

a) To manage Spark streaming jobs.
b) To create and tune machine learning models.
c) To serve as the entry point for reading data and executing queries.
d) To handle fault tolerance and data recovery.

Ans: c) To serve as the entry point for reading data and executing queries.

 

88. What does the 'persist()' method in Apache Spark do?

a) It immediately executes all pending transformations.
b) It saves the application's state for fault tolerance.
c) It allows the user to specify the storage level for an RDD or DataFrame.
d) It aggregates data across different nodes in a cluster.

Ans: c) It allows the user to specify the storage level for an RDD or DataFrame.

 

89. Which of the following is a benefit of Spark's lazy evaluation of transformations?

a) It reduces the number of network transfers.
b) It executes transformations as soon as they are defined.
c) It directly writes data to disk to avoid data loss.
d) It optimizes the overall data processing pipeline.

Ans: d) It optimizes the overall data processing pipeline.

 

90. In Apache Spark, what role does the DAG Scheduler play?

a) It distributes data across the cluster.
b) It converts a user's job into tasks.
c) It schedules tasks to run on various executors.
d) It optimizes queries for execution.

Ans: c) It schedules tasks to run on various executors.

 

91. In Apache Spark, which transformation is used to combine two RDDs with the same key?

a) join()
b) union()
c) merge()
d) combine()

Ans: a) join()

 

92. What is the primary advantage of using DataFrames over RDDs in Spark?

a) DataFrames are easier to serialize.
b) DataFrames are more flexible than RDDs.
c) DataFrames allow for more aggressive optimization.
d) DataFrames support a wider variety of data sources.

Ans: c) DataFrames allow for more aggressive optimization.

 

93. Which action in Spark triggers the execution of the DAG and returns data to the driver?

a) map()
b) reduce()
c) filter()
d) collect()

Ans: d) collect()

 

94. How does Apache Spark achieve fault tolerance?

a) By replicating data across multiple nodes.
b) Through the use of checkpointing.
c) By using Hadoop as its underlying storage system.
d) By storing the lineage of each RDD.

Ans: d) By storing the lineage of each RDD.

 

95. Which one of the following is an action in Apache Spark?

a) map()
b) flatMap()
c) count()
d) filter()

Ans: c) count()

 

96. Which Spark library is designed for scalable, high-throughput, fault-tolerant stream processing of live data streams?

a) Spark SQL
b) Spark Streaming
c) MLlib
d) GraphX

Ans: b) Spark Streaming

 

97. In Apache Spark, which of the following operations is a 'wide' transformation?

a) map()
b) filter()
c) groupBy()
d) flatMap()

Ans: c) groupBy()

 

98. What is the role of the 'Worker Node' in Apache Spark?

a) To manage cluster resources.
b) To store data and execute the tasks assigned by the driver.
c) To coordinate job execution.
d) To optimize execution plans.

Ans: b) To store data and execute the tasks assigned by the driver.

 

99. In Spark, what does the action 'takeSample()' do?

a) It returns a fixed-size sampled subset from an RDD.
b) It takes the first n elements from the RDD and returns them to the driver.
c) It samples an RDD to reduce its size.
d) It creates a new RDD from the sampled elements.

Ans: a) It returns a fixed-size sampled subset from an RDD.

 

100. Which of the following best describes the function of a 'Stage' in Apache Spark?

a) A group of tasks within a job that can be executed in parallel.
b) A single task executed as part of a job.
c) A unit of execution that consists of multiple jobs.
d) A checkpoint in the data processing pipeline.

Ans: a) A group of tasks within a job that can be executed in parallel.

 

101. What is a DataFrame in Apache Spark?

a) A distributed matrix of integers.
b) A type of external storage system.
c) A distributed collection of data organized into named columns.
d) A scheduling component in Spark.

Ans: c) A distributed collection of data organized into named columns.

 

102. In Spark, what is the primary benefit of the DataFrame API over the RDD API?

a) DataFrames are immutable, whereas RDDs are mutable.
b) DataFrames support only structured data, while RDDs support both structured and unstructured data.
c) DataFrames provide optimization through Catalyst Optimizer.
d) DataFrames are slower but more reliable than RDDs.

Ans: c) DataFrames provide optimization through Catalyst Optimizer.

 

103. Which of these is an action in Apache Spark?

a) map()
b) reduce()
c) flatMap()
d) filter()

Ans: b) reduce()

 

104. In Apache Spark, what is the function of the DAG Scheduler?

a) It schedules the execution of stages and tasks.
b) It distributes the application code to the worker nodes.
c) It manages the storage of RDDs in memory.
d) It coordinates communication between nodes.

Ans: a) It schedules the execution of stages and tasks.

 

105. Which command in Apache Spark is used for registering a DataFrame as a temporary view?

a) createOrReplaceTempView()
b) registerDataFrameAsTable()
c) createDataFrame()
d) registerTempTable()

Ans: a) createOrReplaceTempView()

 

106. Which of the following is true about Apache Spark's execution model?

a) It processes data in real-time only.
b) It uses a lazy evaluation model.
c) It executes tasks immediately when they are defined.
d) It does not support in-memory data processing.

Ans: b) It uses a lazy evaluation model.

 

107. In Apache Spark, what is the role of a 'Driver' program?

a) It runs the main() function and is the point of entry of a Spark application.
b) It is responsible for executing tasks on the cluster nodes.
c) It manages the distribution and processing of data across the cluster.
d) It stores the actual data being processed by the application.

Ans: a) It runs the main() function and is the point of entry of a Spark application.

 

108. What does the 'cache()' method do in Apache Spark?

a) It saves the RDD to an external storage system.
b) It persists an RDD in memory.
c) It clears all cached data in the RDD.
d) It redistributes the data across the cluster nodes.

Ans: b) It persists an RDD in memory.

 

109. Which Apache Spark API provides a way to query structured data as a distributed dataset?

a) RDD API
b) DataFrame API
c) Dataset API
d) Broadcast API

Ans: b) DataFrame API

 

110. In Apache Spark, what is an 'action'?

a) An operation that transforms an RDD into another RDD.
b) An operation that triggers the execution of transformations and returns a value.
c) A method for distributing tasks across worker nodes.
d) A technique for persisting data in memory.

Ans: b) An operation that triggers the execution of transformations and returns a value.

 

111. Which feature of Apache Spark allows for processing streams of data in real time?

a) Spark SQL
b) Spark Streaming
c) MLlib
d) GraphX

Ans: b) Spark Streaming

 

112. In Apache Spark, what is a 'narrow transformation'?

a) A transformation that results in data shuffling across partitions.
b) A transformation that does not involve data shuffling.
c) A transformation applied to only a small subset of data.
d) A transformation that reduces the number of partitions.

Ans: b) A transformation that does not involve data shuffling.

 

113. What is the purpose of the SparkContext object in a Spark application?

a) To connect to a Spark cluster and access cluster resources.
b) To store data in memory for fast processing.
c) To schedule and distribute tasks across the cluster.
d) To execute SQL queries on structured data.

Ans: a) To connect to a Spark cluster and access cluster resources.

 

114. In Apache Spark, which action collects the result of RDD computations and sends them back to the driver program?

a) map()
b) collect()
c) reduce()
d) saveAsTextFile()

Ans: b) collect()

 

115. Which Spark component is responsible for converting a logical execution plan into a physical plan?

a) Catalyst Optimizer
b) DAG Scheduler
c) Task Scheduler
d) Cluster Manager

Ans: a) Catalyst Optimizer

 

116. In Apache Spark, what does the 'collect()' action do?

a) It saves the RDD to external storage.
b) It returns a new RDD formed by selecting elements from the current RDD.
c) It returns all elements of the RDD as an array to the driver program.
d) It aggregates the elements of the RDD using a function.

Ans: c) It returns all elements of the RDD as an array to the driver program.

 

117. Which of the following operations will result in a shuffle in Apache Spark?

a) map()
b) filter()
c) groupBy()
d) mapPartitions()

Ans: c) groupBy()

 

118. In Apache Spark, what is the role of the Driver?

a) It executes tasks on cluster nodes.
b) It is responsible for the execution of the main program and creating the SparkContext.
c) It stores the actual data being processed.
d) It manages the cluster and allocates resources.

Ans: b) It is responsible for the execution of the main program and creating the SparkContext.

 

119. What is a 'stage' in the context of Apache Spark's task execution?

a) A set of tasks in a job that can be executed together.
b) A checkpoint in the execution of a Spark application.
c) A single task that is part of a larger job.
d) A phase where Spark reads data from external storage.

Ans: a) A set of tasks in a job that can be executed together.

 

120. In Apache Spark, what is a 'partition'?

a) A section of a DataFrame or RDD representing a data subset.
b) A single node in the Spark cluster.
c) A replicated piece of data for fault tolerance.
d) A unit of execution within a Spark job.

Ans: a) A section of a DataFrame or RDD representing a data subset.

 

121. What is the main advantage of Spark's in-memory processing capability?

a) It ensures data integrity.
b) It offers high data durability.
c) It provides faster data processing compared to disk-based systems.
d) It automatically manages memory allocation.

Ans: c) It provides faster data processing compared to disk-based systems.

 

122. In Apache Spark, what does the 'flatMap()' transformation do?

a) It merges multiple RDDs into a single RDD.
b) It applies a function to each element and flattens the result.
c) It filters elements based on a specified condition.
d) It maps each input record to a key-value pair.

Ans: b) It applies a function to each element and flattens the result.

 

123. Which of the following is not a core component of Apache Spark?

a) Spark SQL
b) Spark Streaming
c) Hadoop Distributed File System (HDFS)
d) MLlib

Ans: c) Hadoop Distributed File System (HDFS)

 

124. How does Apache Spark achieve fault tolerance?

a) Through data replication on multiple nodes.
b) By restarting failed tasks on different nodes.
c) Through lineage information to rebuild lost data.
d) By storing data in a fault-tolerant file system like HDFS.

Ans: c) Through lineage information to rebuild lost data.

 

125. What is the role of the DAG Scheduler in Spark's architecture?

a) It manages the storage of RDDs.
b) It schedules the execution of stages based on RDD dependencies.
c) It allocates system resources to various Spark jobs.
d) It manages the distribution of data across the cluster.

Ans: b) It schedules the execution of stages based on RDD dependencies.

 

126. In Apache Spark, what does an 'RDD' stand for?

a) Rapid Data Deployment
b) Resilient Distributed Dataset
c) Reliable Data Distribution
d) Real-time Data Delivery

Ans: b) Resilient Distributed Dataset

 

127. Which of the following operations in Spark results in a shuffle?

a) map()
b) filter()
c) reduceByKey()
d) flatMap()

Ans: c) reduceByKey()

 

128. What is the purpose of the SparkContext object in a Spark application?

a) To manage Spark streaming jobs.
b) To serve as the entry point for reading data and executing queries.
c) To create and manage RDDs.
d) To establish a connection to a Spark cluster.

Ans: d) To establish a connection to a Spark cluster.

 

129. In Apache Spark, which feature allows for efficient data sharing across different nodes in the cluster?

a) Broadcast variables
b) Accumulators
c) SparkContext
d) Lineage graph

Ans: a) Broadcast variables

 

130. Which of the following is a characteristic of Apache Spark's lazy evaluation?

a) Immediate execution of transformations.
b) Reduced number of I/O operations.
c) Direct writing of intermediate results to disk.
d) Enhanced fault tolerance through data replication.

Ans: b) Reduced number of I/O operations.

 

131. What is the purpose of the 'partitionBy()' method in Apache Spark?

a) It merges two datasets based on a key.
b) It repartitions an RDD based on the specified partitioner.
c) It applies a function to each partition of an RDD.
d) It filters elements from an RDD based on a partition condition.

Ans: b) It repartitions an RDD based on the specified partitioner.

 

132. In Spark, what is the function of an Accumulator?

a) To distribute a large dataset across the cluster.
b) To store data persistently in memory or on disk.
c) To aggregate values from worker nodes back to the driver.
d) To manage the execution of tasks across the cluster.

Ans: c) To aggregate values from worker nodes back to the driver.

 

133. Which of the following best describes a Transformation in Spark?

a) An operation that produces an RDD as output.
b) An operation that triggers execution of the Spark application.
c) An operation that saves data to an external storage system.
d) An operation that modifies the Spark configuration settings.

Ans: a) An operation that produces an RDD as output.

 

134. In Apache Spark, which component is primarily responsible for scheduling jobs and allocating tasks to executors?

a) DAG Scheduler
b) Task Scheduler
c) Cluster Manager
d) SparkContext

Ans: b) Task Scheduler

 

135. Which API in Spark allows for the processing and analysis of structured data using SQL queries?

a) RDD API
b) DataFrame API
c) Dataset API
d) MLlib

Ans: b) DataFrame API

 

136. Which action in Spark triggers the evaluation of RDD transformations?

a) map()
b) reduce()
c) filter()
d) persist()

Ans: b) reduce()

 

137. In Apache Spark, what is the primary function of the Driver Program?

a) To manage and store data.
b) To run the application's main function and create the SparkContext.
c) To perform data processing tasks on the nodes.
d) To schedule jobs across the cluster.

Ans: b) To run the application's main function and create the SparkContext.

 

138. What does the 'persist()' method do in Apache Spark?

a) It saves an RDD, DataFrame, or Dataset to an external storage system.
b) It allows an RDD, DataFrame, or Dataset to be stored using a specified storage level.
c) It replicates the dataset across multiple nodes for fault tolerance.
d) It broadcasts a variable to all worker nodes.

Ans: b) It allows an RDD, DataFrame, or Dataset to be stored using a specified storage level.

 

139. In Spark, what is the purpose of a Partitioner?

a) To distribute data across different nodes in a cluster.
b) To merge data from different RDDs.
c) To optimize queries in Spark SQL.
d) To store data persistently.

Ans: a) To distribute data across different nodes in a cluster.

 

140. What is a DataFrame in Apache Spark?

a) A distributed collection of data organized into named columns.
b) A special type of RDD for handling streaming data.
c) A low-level abstraction for distributed computation.
d) A data structure for graph processing.

Ans: a) A distributed collection of data organized into named columns.

 

141. Which component of Apache Spark is responsible for optimizing logical plans into physical execution plans?

a) SparkContext
b) DAG Scheduler
c) Catalyst Optimizer
d) Task Scheduler

Ans: c) Catalyst Optimizer

 

142. In Apache Spark, what is the significance of 'lazy evaluation'?

a) It immediately computes results as soon as a transformation is called.
b) It postpones computation until an action is called, optimizing the overall data processing workflow.
c) It speeds up the computation by using more memory.
d) It refers to the execution of tasks in a non-sequential order.

Ans: b) It postpones computation until an action is called, optimizing the overall data processing workflow.

 

143. What type of processing does Apache Spark's RDD (Resilient Distributed Dataset) primarily support?

a) Real-time processing
b) Batch processing
c) Both batch and real-time processing
d) Transactional data processing

Ans: b) Batch processing

 

144. In Apache Spark, which operation will most likely cause a shuffle?

a) map()
b) filter()
c) reduceByKey()
d) mapPartitions()

Ans: c) reduceByKey()

 

145. What is the role of a 'Stage' in the context of Apache Spark's execution model?

a) A collection of tasks that can be executed in parallel.
b) A phase where Spark reads data from external storage.
c) The individual task execution within a job.
d) A unit of computation in the DAG.

Ans: a) A collection of tasks that can be executed in parallel.

 

146. In Apache Spark, what is the purpose of the 'reduce()' action?

a) To filter elements of the RDD based on a condition.
b) To merge the elements of the RDD using a specified associative function.
c) To apply a function to each element of the RDD and return a new RDD.
d) To save the RDD to an external storage system.

Ans: b) To merge the elements of the RDD using a specified associative function.

 

147. Which of the following is true about Spark's 'saveAsTextFile' action?

a) It saves the RDD contents as a single text file.
b) It saves each RDD partition as a separate text file in a directory.
c) It converts RDDs into DataFrames before saving.
d) It is used to save streaming data into text files.

Ans: b) It saves each RDD partition as a separate text file in a directory.

 

148. In Apache Spark, which of these is a transformation operation?

a) collect()
b) count()
c) map()
d) take()

Ans: c) map()

 

149. What is the primary use case of Apache Spark's MLlib library?

a) To process graph data.
b) To perform real-time data processing.
c) For machine learning.
d) To handle structured data queries.

Ans: c) For machine learning.

 

150. In Apache Spark, what does 'SparkSession' provide?

a) A way to interact with various Spark functionalities like Spark SQL and DataFrames.
b) The functionality for graph processing and analysis.
c) Real-time data streaming capabilities.
d) Machine learning algorithms and tools.

Ans: a) A way to interact with various Spark functionalities like Spark SQL and DataFrames.