| Join Telegram |  |
| Join Whatsapp Groups |  |
Apache Spark MCQs and Answers With Explanation: Apache Spark is an open-source distributed computing system designed for processing large-scale data processing tasks. It is widely used for big data processing, machine learning, and real-time streaming analytics. If you are looking to enhance your skills in Apache Spark, then you have come to the right place.
Apache Spark MCQs and Answers
In this article, we have compiled a list of the top 55 Apache Spark MCQs and Answers to help you test your knowledge and prepare for any Apache Spark quiz or exam. These Apache Spark Multiple Choice Questions and Answers come with detailed explanations to help you understand the concepts better. So, let’s dive into the world of Apache Spark MCQs and improve our skills.
Apache Spark Quiz
| Name | Apache Spark |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 55 Apache Spark MCQs
1. What is Apache Spark?
a) A big data processing engine
b) A machine learning algorithm
c) A data visualization tool
d) A database management system
Answer: a) A big data processing engine
Explanation: Apache Spark is an open-source, distributed big data processing engine that can handle large-scale data processing tasks in real-time. It provides an interface for programming clusters with implicit data parallelism and fault tolerance.
2. Which programming languages are supported by Apache Spark?
a) Java, Python, and Scala
b) Java, Python, and C#
c) Java, PHP, and Scala
d) Java, Python, and Ruby
Answer: a) Java, Python, and Scala
Explanation: Apache Spark supports multiple programming languages, including Java, Python, and Scala. This makes it a popular choice for big data processing as it can be used with languages that are popular among data scientists and big data developers.
3. Which of the following is NOT a feature of Apache Spark?
a) Batch processing
b) Stream processing
c) Graph processing
d) Word processing
Answer: d) Word processing
Explanation: Word processing is not a feature of Apache Spark. The three main features of Apache Spark are batch processing, stream processing, and graph processing.
4. What is the default storage level in Spark?
a) MEMORY_ONLY
b) DISK_ONLY
c) MEMORY_AND_DISK
d) OFF_HEAP
Answer: a) MEMORY_ONLY
Explanation: MEMORY_ONLY is the default storage level in Spark. It stores RDDs (Resilient Distributed Datasets) in memory as deserialized Java objects. This provides fast access to data but can be memory-intensive.
5. What is an RDD?
a) A Resilient Distributed Dataset
b) A Remote Data Depot
c) A Replicated Database Descriptor
d) A Recursive Data Definition
Answer: a) A Resilient Distributed Dataset
Explanation: An RDD (Resilient Distributed Dataset) is a fault-tolerant collection of elements that can be processed in parallel across a cluster. RDDs can be created from Hadoop Distributed File System (HDFS) files, local files, or other data sources.
6. What is lazy evaluation in Spark?
a) Spark processes data as soon as it is received
b) Spark processes data only when it is needed
c) Spark processes data asynchronously
d) Spark processes data in parallel
Answer: b) Spark processes data only when it is needed
Explanation: Lazy evaluation is a feature of Spark that allows it to delay the execution of operations until the data is needed. This helps to optimize the performance of Spark jobs by reducing unnecessary processing.
7. Which of the following is NOT a transformation operation in Spark?
a) map
b) reduce
c) filter
d) join
Answer: b) reduce
Explanation: reduce is not a transformation operation in Spark. It is an action operation that aggregates the elements of an RDD using a specified function.
8. What is an action operation in Spark?
a) An operation that transforms an RDD into another RDD
b) An operation that aggregates the elements of an RDD
c) An operation that filters the elements of an RDD
d) An operation that maps the elements of an RDD to a new value
Answer: b) An operation that aggregates the elements of an RDD
Explanation: An action operation in Spark is an operation that triggers the computation of an RDD and returns a result to the driver program. Examples of action operations include reduce, collect, and count.
9. What is a DAG in Spark?
a) A Directed Acyclic Graph
b) A Distributed Analysis Graph
c) A Data Access Graph
d) A Dynamic Aggregation Graph
Answer: a) A Directed Acyclic Graph
Explanation: A DAG (Directed Acyclic Graph) in Spark is a data structure that represents the logical execution plan of a Spark job. It consists of a set of transformations and actions that are arranged in a directed acyclic graph to optimize the computation of the job.
10. Which of the following is NOT a cluster manager that can be used with Spark?
a) Hadoop YARN
b) Apache Mesos
c) Kubernetes
d) Apache Cassandra
Answer: d) Apache Cassandra
Explanation: Apache Cassandra is not a cluster manager that can be used with Spark. The three main cluster managers that can be used with Spark are Hadoop YARN, Apache Mesos, and Kubernetes.
11. What is a shuffle in Spark?
a) A process of redistributing data across partitions
b) A process of reordering data within a partition
c) A process of compressing data before storage
d) A process of caching data in memory
Answer: a) A process of redistributing data across partitions
Explanation: A shuffle in Spark is a process of redistributing data across partitions. It is required when data needs to be aggregated or joined across multiple partitions.
12. Which of the following is a serialization format that can be used with Spark?
a) JSON
b) XML
c) CSV
d) All of the above
Answer: d) All of the above
Explanation: Spark supports multiple serialization formats, including JSON, XML, and CSV. This allows data to be stored and processed in a variety of formats.
13. What is the purpose of a checkpoint in Spark?
a) To optimize the performance of Spark jobs
b) To cache data in memory
c) To recover from failures during job execution
d) To convert data from one format to another
Answer: c) To recover from failures during job execution
Explanation: A checkpoint in Spark is a mechanism that allows the state of an RDD to be saved to disk to recover from failures during job execution. Checkpoints can also help to optimize the performance of Spark jobs by reducing the amount of recomputation required.
14. What is a broadcast variable in Spark?
a) A variable that is shared across all nodes in a cluster
b) A variable that is cached in memory
c) A variable that is serialized and sent to all nodes in a cluster
d) A variable that is used to partition data in an RDD
Answer: c) A variable that is serialized and sent to all nodes in a cluster
Explanation: A broadcast variable in Spark is a read-only variable that is serialized and sent to all nodes in a cluster to avoid the need for multiple copies of the same data.
15. Which of the following is a machine learning library that can be used with Spark?
a) TensorFlow
b) Scikit-learn
c) Keras
d) MLlib
Answer: d) MLlib
Explanation: MLlib is a machine learning library that can be used with Spark to perform various machine learning tasks, including classification, regression, clustering, and collaborative filtering.
16. What is the default parallelism level in Spark?
a) The number of nodes in the cluster
b) The number of cores on each node in the cluster
c) The sum of the number of cores on all nodes in the cluster
d) The maximum number of cores on any node in the cluster
Answer: c) The sum of the number of cores on all nodes in the cluster
Explanation: The default parallelism level in Spark is determined by the sum of the number of cores on all nodes in the cluster. This allows Spark to maximize the utilization of the available resources in the cluster.
17. Which of the following is NOT a type of transformation in Spark?
a) map
b) filter
c) reduce
d) join
Answer: c) reduce
Explanation: Reduce is not a type of transformation in Spark. It is an action that performs an aggregation operation on an RDD.
18. What is the difference between an action and a transformation in Spark?
a) An action returns a new RDD, while a transformation modifies an existing RDD.
b) An action performs a computation and returns a result, while a transformation creates a new RDD.
c) An action is lazy, while a transformation is eager.
d) An action can be cached in memory, while a transformation cannot.
Answer: b) An action performs a computation and returns a result, while a transformation creates a new RDD.
Explanation: An action in Spark performs a computation on an RDD and returns a result, while a transformation creates a new RDD by applying a function to each element of an existing RDD.
19. What is the difference between a DataFrame and an RDD in Spark?
a) A DataFrame is a distributed collection of data organized into named columns, while an RDD is a distributed collection of unstructured data.
b) A DataFrame is an immutable distributed collection of data, while an RDD is a mutable distributed collection of data.
c) A DataFrame supports SQL queries, while an RDD does not.
d) All of the above.
Answer: a) A DataFrame is a distributed collection of data organized into named columns, while an RDD is a distributed collection of unstructured data.
Explanation: A DataFrame in Spark is a distributed collection of data organized into named columns, similar to a table in a relational database. An RDD, on the other hand, is a distributed collection of unstructured data that can be processed in parallel.
20. Which of the following is a supported programming language for Spark?
a) Python
b) Java
c) Scala
d) All of the above.
Answer: d) All of the above.
Explanation: Spark supports multiple programming languages, including Python, Java, and Scala, which allows developers to choose the language that they are most comfortable with.
21. Which of the following is a type of RDD in Spark?
a) HadoopRDD
b) DataFrame
c) Dataset
d) DataFrameReader
Answer: a) HadoopRDD
Explanation: HadoopRDD is a type of RDD in Spark that represents an RDD that is stored in Hadoop Distributed File System (HDFS).
22. Which of the following is a type of cache in Spark?
a) Memory-only cache
b) Disk-only cache
c) Memory and disk cache
d) All of the above
Answer: d) All of the above
Explanation: Spark supports multiple types of cache, including memory-only cache, disk-only cache, and memory and disk cache, which allows developers to choose the caching strategy that best suits their needs.
23. What is a partition in Spark?
a) A subset of data stored in memory
b) A subset of data stored on disk
c) A subset of data that can be processed in parallel
d) A subset of data that is cached in memory
Answer: c) A subset of data that can be processed in parallel
Explanation: A partition in Spark is a subset of data that can be processed in parallel. An RDD can be partitioned across multiple nodes in a cluster to enable parallel processing.
24. What is a lineage in Spark?
a) A history of transformations that have been applied to an RDD
b) A list of all the nodes in a Spark cluster
c) A list of all the actions that have been performed on an RDD
d) A list of all the data sources that have been used to create an RDD
Answer: a) A history of transformations that have been applied to an RDD
Explanation: Lineage in Spark refers to the history of transformations that have been applied to an RDD. It is used to recover lost data in case of failures and to optimize the execution plan of Spark jobs.
25. What is the difference between persist() and cache() methods in Spark?
a) persist() is used to cache an RDD in memory or on disk, while cache() is used to cache an RDD only in memory.
b) cache() is used to cache an RDD in memory or on disk, while persist() is used to cache an RDD only in memory.
c) persist() is an action, while cache() is a transformation.
d) There is no difference between persist() and cache() methods.
Answer: a) persist() is used to cache an RDD in memory or on disk, while cache() is used to cache an RDD only in memory.
Explanation: Both persist() and cache() methods are used to cache an RDD in memory for faster processing, but persist() also allows for caching on disk.
26. What is a Spark driver?
a) A node in a Spark cluster that manages the execution of Spark applications
b) A type of RDD in Spark
c) A function that applies a transformation to an RDD
d) A function that applies an action to an RDD
Answer: a) A node in a Spark cluster that manages the execution of Spark applications
Explanation: The Spark driver is the process that manages the execution of Spark applications in a cluster. It schedules tasks, coordinates with workers, and monitors the progress of the application.
27. What is a Spark worker?
a) A node in a Spark cluster that runs tasks on RDDs
b) A type of RDD in Spark
c) A function that applies a transformation to an RDD
d) A function that applies an action to an RDD
Answer: a) A node in a Spark cluster that runs tasks on RDDs
Explanation: A Spark worker is a node in a Spark cluster that runs tasks on RDDs. Workers are responsible for executing tasks assigned by the Spark driver and returning the results.
28. What is a Spark executor?
a) A process that runs on a worker node and performs tasks assigned by the Spark driver
b) A type of RDD in Spark
c) A function that applies a transformation to an RDD
d) A function that applies an action to an RDD
Answer: a) A process that runs on a worker node and performs tasks assigned by the Spark driver
Explanation: A Spark executor is a process that runs on a worker node and performs tasks assigned by the Spark driver. Each executor is responsible for executing one or more tasks assigned by the driver.
29. What is a Spark stage?
a) A set of tasks that can be executed in parallel
b) A set of transformations applied to an RDD
c) A set of nodes in a Spark cluster
d) A set of actions performed
Answer: a) A set of tasks that can be executed in parallel
Explanation: A Spark stage is a set of tasks that can be executed in parallel. A stage is created whenever there is a shuffle in the RDD and the data needs to be transferred across the network.
30. Which of the following operations is a transformation in Spark?
a) reduce()
b) collect()
c) filter()
d) count()
Answer: c) filter()
Explanation: In Spark, transformations are operations that create a new RDD from an existing one without changing the original RDD. Examples of transformations include filter(), map(), flatMap(), and groupByKey().
31. Which of the following operations is an action in Spark?
a) map()
b) filter()
c) count()
d) flatMap()
Answer: c) count()
Explanation: In Spark, actions are operations that trigger the computation of an RDD and return a result or write data to an external storage system. Examples of actions include count(), collect(), reduce(), and save().
32. What is a DataFrame in Spark?
a) A distributed collection of data organized into named columns
b) A distributed collection of key-value pairs
c) A distributed collection of immutable objects
d) A distributed collection of binary data
Answer: a) A distributed collection of data organized into named columns
Explanation: A DataFrame in Spark is a distributed collection of data organized into named columns. It is similar to a table in a relational database or a spreadsheet in Excel.
33. What is a Spark SQL?
a) A Spark library for machine learning
b) A Spark library for graph processing
c) A Spark library for stream processing
d) A Spark module for working with structured and semi-structured data
Answer: d) A Spark module for working with structured and semi-structured data
Explanation: Spark SQL is a Spark module for working with structured and semi-structured data using SQL-like syntax. It provides a programming interface to work with structured data using SQL, as well as a DataFrame API for working with both structured and semi-structured data.
34. Which of the following is a benefit of using Spark SQL?
a) Faster data processing
b) Better support for unstructured data
c) Easier integration with NoSQL databases
d) More flexibility in data processing operations
Answer: a) Faster data processing
Explanation: Spark SQL can offer faster data processing compared to traditional SQL-based systems due to its in-memory processing capabilities and distributed architecture.
35. What is a Spark Streaming?
a) A Spark module for batch processing
b) A Spark module for real-time stream processing
c) A Spark module for graph processing
d) A Spark module for machine learning
Answer: b) A Spark module for real-time stream processing
Explanation: Spark Streaming is a Spark module for real-time stream processing. It enables developers to process data streams in near real-time using Spark’s in-memory processing engine.
36. Which of the following is a benefit of using Spark Streaming?
a) Easier integration with NoSQL databases
b) More flexibility in data processing operations
c) Better support for batch processing
d) Real-time processing of data streams
Answer: d) Real-time processing of data streams
Explanation: Spark Streaming allows for real-time processing of data streams, which is useful for applications such as real-time analytics, monitoring, and alerting.
37. Which of the following is a common use case for Spark Streaming?
a) Fraud detection
b) Recommender systems
c) Sentiment analysis
d) Image recognition
Answer: a) Fraud detection
Explanation: Spark Streaming is commonly used for fraud detection applications that require real-time processing of large volumes of data streams.
38. What is a Spark ML?
a) A library for machine learning in Spark
b) A module for stream processing in Spark
c) A module for working with structured and semi-structured data in Spark
d) A module for batch processing in Spark
Answer: a) A library for machine learning in Spark
Explanation: Spark ML is a library for machine learning in Spark that provides a set of high-level APIs built on top of DataFrames.
39. Which of the following is a benefit of using Spark ML for machine learning?
a) Ability to scale to large datasets
b) Better support for unstructured data
c) Easier integration with NoSQL databases
d) More flexibility in data processing operations
Answer: a) Ability to scale to large datasets
Explanation: Spark ML provides the ability to scale machine learning algorithms to large datasets using Spark’s distributed computing capabilities.
40. Which of the following is a supervised learning algorithm in Spark ML?
a) K-means clustering
b) Logistic regression
c) Random forest
d) Principal component analysis
Answer: b) Logistic regression
Explanation: Logistic regression is a supervised learning algorithm that is available in Spark ML.
41. Which of the following is an unsupervised learning algorithm in Spark ML?
a) Decision trees
b) Naive Bayes
c) K-means clustering
d) Gradient boosting
Answer: c) K-means clustering
Explanation: K-means clustering is an unsupervised learning algorithm that is available in Spark ML.
42. What is the difference between RDDs and DataFrames in Spark?
a) RDDs have a schema, while DataFrames do not
b) DataFrames are immutable, while RDDs are mutable
c) RDDs are strongly typed, while DataFrames are weakly typed
d) DataFrames are more efficient for SQL-like operations
Answer: d) DataFrames are more efficient for SQL-like operations
Explanation: DataFrames in Spark are more efficient for SQL-like operations compared to RDDs. DataFrames also have a schema that defines the structure of the data, while RDDs do not.
43. What is a Spark master?
a) A process that runs on a node in a Spark cluster and executes tasks
b) A process that manages the Spark driver
c) The process that coordinates the execution of a Spark application
d) The node that manages the allocation of resources in a Spark cluster
Answer: d) The node that manages the allocation of resources in a Spark cluster
Explanation: The Spark master is the node that manages the allocation of resources in a Spark cluster. It is responsible for scheduling tasks and coordinating the allocation of resources to different applications.
44. Which of the following is a feature of Spark Streaming?
a) Ability to process data in batches
b) Support for SQL-like queries
c) Integration with Hadoop Distributed File System (HDFS)
d) Automatic fault tolerance and recovery
Answer: a) Ability to process data in batches
Explanation: Spark Streaming is a module for stream processing in Spark that allows for the processing of data in small batches.
45. Which of the following is a feature of Spark SQL?
a) Ability to process data in real-time
b) Support for machine learning algorithms
c) Integration with Hadoop Distributed File System (HDFS)
d) Support for SQL-like queries
Answer: d) Support for SQL-like queries
Explanation: Spark SQL provides support for SQL-like queries on structured and semi-structured data.
46. What is the main difference between Spark Streaming and Spark SQL?
a) Spark Streaming is designed for batch processing, while Spark SQL is designed for stream processing
b) Spark Streaming is designed for stream processing, while Spark SQL is designed for batch processing
c) Spark Streaming provides support for SQL-like queries, while Spark SQL provides support for machine learning algorithms
d) Spark Streaming provides support for real-time data processing, while Spark SQL does not
Answer: b) Spark Streaming is designed for stream processing, while Spark SQL is designed for batch processing
Explanation: Spark Streaming is designed for stream processing, while Spark SQL is designed for batch processing.
47. What is a Spark cluster manager?
a) A process that runs on a node in a Spark cluster and executes tasks
b) A process that manages the Spark driver
c) A process that manages the Spark master
d) A software that manages the allocation of resources in a Spark cluster
Answer: d) A software that manages the allocation of resources in a Spark cluster
Explanation: A Spark cluster manager is a software that manages the allocation of resources in a Spark cluster. Examples include Apache Mesos, Hadoop YARN, and Spark Standalone.
48. Which of the following is a feature of Spark’s GraphX library?
a) Support for graph algorithms
b) Support for SQL-like queries
c) Integration with Hadoop Distributed File System (HDFS)
d) Support for machine learning algorithms
Answer: a) Support for graph algorithms
Explanation: Spark’s GraphX library provides support for graph algorithms, including PageRank and triangle counting.
49. Which of the following is not a component of Spark’s core engine?
a) Spark Streaming
b) Spark SQL
c) Spark MLlib
d) Spark GraphX
Answer: a) Spark Streaming
Explanation: Spark Streaming is not a component of Spark’s core engine, but rather a module for stream processing built on top of the core engine.
50. What is the default persistence level for RDDs in Spark?
a) MEMORY_ONLY
b) MEMORY_ONLY_SER
c) DISK_ONLY
d) MEMORY_AND_DISK
Answer: a) MEMORY_ONLY
Explanation: The default persistence level for RDDs in Spark is MEMORY_ONLY.
51. What is a Spark job?
a) A set of tasks that are executed in parallel on a Spark cluster
b) A data structure that represents a distributed collection of data
c) The process of submitting a Spark application to a cluster for execution
d) A process that manages the allocation of resources in a Spark cluster
Answer: c) The process of submitting a Spark application to a cluster for execution
Explanation: A Spark job refers to the process of submitting a Spark application to a cluster for execution.
52. Which of the following is a feature of Spark MLlib?
a) Support for graph algorithms
b) Support for SQL-like queries
c) Integration with Hadoop Distributed File System (HDFS)
d) Support for machine learning algorithms
Answer: d) Support for machine learning algorithms
Explanation: Spark MLlib is a module for machine learning in Spark that provides support for a variety of machine learning algorithms, including classification, regression, and clustering.
53. What is the main difference between Spark SQL and traditional SQL?
a) Spark SQL supports only a subset of the SQL language
b) Spark SQL is designed for distributed processing
c) Spark SQL does not support joins
d) Spark SQL does not support indexing
Answer: b) Spark SQL is designed for distributed processing
Explanation: The main difference between Spark SQL and traditional SQL is that Spark SQL is designed for distributed processing, allowing it to handle much larger datasets than traditional SQL.
54. What is a Spark application?
a) A set of tasks that are executed in parallel on a Spark cluster
b) A data structure that represents a distributed collection of data
c) A program that uses Spark APIs to perform some computation
d) A process that manages the allocation of resources in a Spark cluster
Answer: c) A program that uses Spark APIs to perform some computation
Explanation: A Spark application is a program that uses Spark APIs to perform some computation, such as processing data or running machine learning algorithms.
55. What is the role of the Spark driver program?
a) To execute tasks on worker nodes in a Spark cluster
b) To manage the allocation of resources in a Spark cluster
c) To parse user input and generate an execution plan for Spark
d) To manage the execution of Spark jobs
Answer: c) To parse user input and generate an execution plan for Spark
Explanation: The Spark driver program is responsible for parsing user input and generating an execution plan for Spark.
The Apache Spark MCQs and Answers with explanations provide an excellent opportunity to enhance your skills in distributed computing and big data processing. Practice these questions to improve your knowledge and excel in Apache Spark quizzes and exams. To gain more knowledge in this field, be sure to follow us at freshersnow.com.