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MapReduce MCQs and Answers With Explanation – MapReduce is a programming model used to process and analyze large data sets in a distributed computing environment. It was developed by Google to support parallel computation on large data sets across clusters of computers. MapReduce is widely used for data-intensive computing tasks and is a core component of Apache Hadoop, an open-source distributed computing framework. Hadoop MapReduce MCQ Questions And Answers are designed to test users’ knowledge and understanding of MapReduce’s features and functionalities.
MapReduce MCQ Questions And Answers
These MapReduce Multiple Choice Questions will explore topics such as data processing, parallel computing, and distributed computing, among others. By answering these MapReduce MCQ Questions, users can improve their proficiency in using MapReduce and leverage its capabilities to derive insights from large datasets.
MapReduce Multiple Choice Questions
| Name | MapReduce |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 45 MapReduce Quiz Questions | Practice Online Quiz
1. What is MapReduce?
A. A distributed data processing framework
B. A single computer data processing framework
C. A database management system
D. A network routing algorithm
Answer: A. A distributed data processing framework
Explanation: MapReduce is a distributed data processing framework that allows developers to process large amounts of data in parallel across a cluster of commodity hardware.
2. Which of the following is true about MapReduce?
A. It is a batch processing system.
B. It is a real-time processing system.
C. It can only process structured data.
D. It can only process data stored in a Hadoop Distributed File System (HDFS).
Answer: A. It is a batch processing system.
Explanation: MapReduce is a batch processing system that processes data in large batches, rather than in real-time.
3. Which of the following is true about MapReduce jobs?
A. They consist of only one map task and one reduce task.
B. They can have multiple map tasks and reduce tasks.
C. They can only have one reduce task.
D. They can only have one map task.
Answer: B. They can have multiple map tasks and reduce tasks.
Explanation: MapReduce jobs can have multiple map tasks and reduce tasks, allowing them to process large amounts of data in parallel.
4. What is the purpose of the map function in MapReduce?
A. To convert input data into key-value pairs
B. To sort the input data
C. To combine the input data
D. To summarize the input data
Answer: A. To convert input data into key-value pairs
Explanation: The map function in MapReduce is used to convert input data into key-value pairs, which are then passed on to the reduce function for further processing.
5. What is the purpose of the reduce function in MapReduce?
A. To sort the input data
B. To combine the input data
C. To summarize the input data
D. To convert input data into key-value pairs
Answer: C. To summarize the input data
Explanation: The reduce function in MapReduce is used to summarize the input data, by performing operations such as counting, summing, or averaging.
6. Which of the following is true about the shuffle phase in MapReduce?
A. It sorts the output of the map phase.
B. It sorts the output of the reduce phase.
C. It combines the output of the map phase.
D. It combines the output of the reduce phase.
Answer: A. It sorts the output of the map phase.
Explanation: The shuffle phase in MapReduce sorts the output of the map phase, and groups the output by key so that it can be passed on to the reduce phase for further processing.
7. Which of the following is true about the combiner function in MapReduce?
A. It is the same as the reduce function.
B. It is run after the reduce function.
C. It is run after the map function.
D. It is run before the reduce function.
Answer: D. It is run before the reduce function.
Explanation: The combiner function in MapReduce is run before the reduce function, and is used to combine intermediate key-value pairs before they are passed on to the reduce function.
8. Which of the following is true about the partitioner function in MapReduce?
A. It is used to sort the output of the map phase.
B. It is used to group the output of the map phase by key.
C. It is used to divide the output of the map phase into partitions.
D. It is used to combine the output of the map phase.
Answer: C. It is used to divide the output of the map phase into partitions.
Explanation: The partitioner function in MapReduce is used to divide the output of the map phase into partitions based on the key, which allows the reduce phase to process the data in parallel.
9. Which of the following is a disadvantage of using MapReduce?
A. It can only process small amounts of data.
B. It requires specialized hardware.
C. It has a high latency.
D. It is difficult to use.
Answer: C. It has a high latency.
Explanation: MapReduce has a high latency, meaning that it can take a long time to process large amounts of data.
10. Which of the following is an advantage of using MapReduce?
A. It requires specialized hardware.
B. It can only process small amounts of data.
C. It can process data in parallel.
D. It is difficult to use.
Answer: C. It can process data in parallel.
Explanation: MapReduce can process data in parallel, allowing it to process large amounts of data quickly and efficiently.
11. Which of the following is true about Hadoop?
A. It is a distributed data processing framework.
B. It is a real-time processing system.
C. It can only process structured data.
D. It is a database management system.
Answer: A. It is a distributed data processing framework.
Explanation: Hadoop is a distributed data processing framework that is built on top of the MapReduce framework.
12. Which of the following is a component of Hadoop?
A. MapReduce
B. HBase
C. Hive
D. All of the above
Answer: D. All of the above
Explanation: MapReduce, HBase, and Hive are all components of the Hadoop ecosystem.
13. Which of the following is a key feature of Hadoop?
A. High latency
B. Real-time processing
C. Fault tolerance
D. Limited scalability
Answer: C. Fault tolerance
Explanation: Hadoop is designed to be fault-tolerant, meaning that it can continue to operate even if one or more nodes in the cluster fail.
14. Which of the following is true about HDFS?
A. It is a database management system.
B. It can only store structured data.
C. It is a distributed file system.
D. It is a real-time processing system.
Answer: C. It is a distributed file system.
Explanation: HDFS is a distributed file system that is used to store large amounts of data across a cluster of commodity hardware.
15. Which of the following is a key feature of HDFS?
A. High latency
B. Real-time processing
C. Fault tolerance
D. Limited scalability
Answer: C. Fault tolerance
Explanation: HDFS is designed to be fault-tolerant, meaning that it can continue to operate even if one or more nodes in the cluster fail.
16. Which of the following is true about HBase?
A. It is a distributed file system.
B. It is a database management system.
C. It is a real-time processing system.
D. It can only store structured data.
Answer: B. It is a database management system.
Explanation: HBase is a distributed, column-oriented database management system that is built on top of Hadoop.
17. Which of the following is true about Hive?
A. It is a distributed file system.
B. It is a database management system.
C. It is a real-time processing system.
D. It is a data warehouse system.
Answer: D. It is a data warehouse system.
Explanation: Hive is a data warehouse system that is built on top of Hadoop, and is used to query and analyze large datasets stored in HDFS.
18. Which of the following is true about Pig?
A. It is a distributed file system.
B. It is a database management system.
C. It is a programming language for processing data.
D. It is a real-time processing system.
Answer: C. It is a programming language for processing data.
Explanation: Pig is a high-level programming language that is used to process large datasets in Hadoop.
19. Which of the following is true about YARN?
A. It is a distributed file system.
B. It is a database management system.
C. It is a real-time processing system.
D. It is a resource management system.
Answer: D. It is a resource management system.
Explanation: YARN (Yet Another Resource Negotiator) is a resource management system that is used in Hadoop to manage the resources of the cluster and allocate them to running applications.
20. Which of the following is true about Spark?
A. It is a distributed data processing framework.
B. It is built on top of Hadoop.
C. It can only process structured data.
D. It is a real-time processing system.
Answer: A. It is a distributed data processing framework.
Explanation: Spark is a distributed data processing framework that is designed to be fast and efficient, and is built on top of Hadoop.
21. Which of the following is a key feature of Spark?
A. Real-time processing
B. Fault tolerance
C. High latency
D. Limited scalability
Answer: A. Real-time processing
Explanation: Spark is designed for real-time processing, and can process data in near real-time.
22. Which of the following is true about RDDs in Spark?
A. They are immutable.
B. They can only be processed using SQL queries.
C. They are stored in a distributed file system.
D. They are processed using MapReduce.
Answer: A. They are immutable.
Explanation: RDDs (Resilient Distributed Datasets) in Spark are immutable, meaning that once they are created, they cannot be changed.
23. Which of the following is true about Spark SQL?
A. It is a database management system.
B. It is a programming language for processing data.
C. It is a component of Spark that allows for SQL queries to be executed.
D. It is used for real-time processing.
Answer: C. It is a component of Spark that allows for SQL queries to be executed.
Explanation: Spark SQL is a component of Spark that allows for SQL queries to be executed on Spark data.
24. Which of the following is true about Spark Streaming?
A. It is a component of Spark that allows for batch processing of data.
B. It is a real-time processing system.
C. It is a distributed file system.
D. It is a database management system.
Answer: B. It is a real-time processing system.
Explanation: Spark Streaming is a component of Spark that allows for real-time processing of data.
25. Which of the following is true about Mesos?
A. It is a distributed data processing framework.
B. It is a resource management system.
C. It is a programming language for processing data.
D. It is a database management system.
Answer: B. It is a resource management system.
Explanation: Mesos is a resource management system that is used to manage the resources of a cluster and allocate them to running applications.
26. What is the role of a Combiner function in Hadoop?
A. To combine multiple small files into a larger file.
B. To combine the output of the Mapper function before it is sent to the Reducer function.
C. To split a large file into smaller files.
D. To compress the output of the Mapper function.
Answer: B. To combine the output of the Mapper function before it is sent to the Reducer function.
Explanation: The Combiner function in Hadoop is used to combine the output of the Mapper function before it is sent to the Reducer function. This helps to reduce the amount of data that needs to be transferred over the network.
27. What is the purpose of a Partitioner in Hadoop?
A. To partition the input data for the Mapper function.
B. To partition the output data of the Mapper function before it is sent to the Reducer function.
C. To partition the output data of the Reducer function before it is written to disk.
D. To partition the input data for the Reducer function.
Answer: B. To partition the output data of the Mapper function before it is sent to the Reducer function.
Explanation: The Partitioner in Hadoop is used to partition the output data of the Mapper function before it is sent to the Reducer function. This helps to ensure that data with the same key is sent to the same Reducer.
28. Which of the following is true about Hadoop Streaming?
A. It is a programming language for processing data.
B. It is a component of Hadoop that allows for real-time processing.
C. It is a tool that allows for non-Java applications to be run in Hadoop.
D. It is a database management system.
Answer: C. It is a tool that allows for non-Java applications to be run in Hadoop.
Explanation: Hadoop Streaming is a tool that allows for non-Java applications to be run in Hadoop by using standard input and output streams.
29. Which of the following is true about Avro?
A. It is a distributed data processing framework.
B. It is a file format used for storing data in Hadoop.
C. It is a programming language for processing data.
D. It is a real-time processing system.
Answer: B. It is a file format used for storing data in Hadoop.
Explanation: Avro is a file format used for storing data in Hadoop that is designed to be compact and efficient.
30. Which of the following is true about Pig Latin?
A. It is a database management system.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a distributed data processing framework.
Answer: B. It is a programming language for processing data.
Explanation: Pig Latin is a high-level programming language for processing data in Hadoop that is similar to SQL.
31. Which of the following is true about Sqoop?
A. It is a tool for real-time processing.
B. It is a programming language for processing data.
C. It is a distributed data processing framework.
D. It is a tool for importing and exporting data between Hadoop and relational databases.
Answer: D. It is a tool for importing and exporting data between Hadoop and relational databases.
Explanation: Sqoop is a tool that is used to import and export data between Hadoop and relational databases.
32. Which of the following is true about Flume?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a tool for collecting, aggregating, and moving large amounts of log data from multiple sources into Hadoop.
Answer: D. It is a tool for collecting, aggregating, and moving large amounts of log data from multiple sources into Hadoop.
Explanation: Flume is a tool that is used for collecting, aggregating, and moving large amounts of log data from multiple sources into Hadoop.
33. Which of the following is true about Oozie?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a workflow scheduling system for managing Hadoop jobs.
Answer: D. It is a workflow scheduling system for managing Hadoop jobs.
Explanation: Oozie is a workflow scheduling system that is used for managing Hadoop jobs. It allows users to define, schedule, and manage Hadoop jobs as a series of interconnected workflows.
34. Which of the following is true about Mahout?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a machine learning library for Hadoop.
Answer: D. It is a machine learning library for Hadoop.
Explanation: Mahout is a machine learning library for Hadoop that provides a set of algorithms for clustering, classification, and collaborative filtering.
35. Which of the following is true about ZooKeeper?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a centralized service for maintaining configuration information, naming, and providing distributed synchronization.
Answer: D. It is a centralized service for maintaining configuration information, naming, and providing distributed synchronization.
Explanation: ZooKeeper is a centralized service that provides a distributed coordination service for maintaining configuration information, naming, providing distributed synchronization, and group services for distributed applications.
36. Which of the following is true about Cascading?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a data processing API for Hadoop that provides a higher-level abstraction than MapReduce.
Answer: D. It is a data processing API for Hadoop that provides a higher-level abstraction than MapReduce.
Explanation: Cascading is a data processing API for Hadoop that provides a higher-level abstraction than MapReduce. It allows developers to build complex data processing pipelines using a simple and intuitive API.
37. Which of the following is true about HCatalog?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a table and storage management layer for Hadoop.
Answer: D. It is a table and storage management layer for Hadoop.
Explanation: HCatalog is a table and storage management layer for Hadoop that provides a unified metadata management system for all Hadoop components.
38. Which of the following is true about Hadoop Distributed File System (HDFS)?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a distributed file system that provides reliable and scalable data storage for Hadoop.
Answer: D. It is a distributed file system that provides reliable and scalable data storage for Hadoop.
Explanation: Hadoop Distributed File System (HDFS) is a distributed file system that provides reliable and scalable data storage for Hadoop.
39. Which of the following is true about Tez?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a data processing framework that provides a higher-level abstraction than MapReduce.
Answer: D. It is a data processing framework that provides a higher-level abstraction than MapReduce.
Explanation: Tez is a data processing framework that provides a higher-level abstraction than MapReduce and allows users to build complex data processing pipelines using a simple and intuitive API.
40. Which of the following is true about Hadoop Common?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a set of common utilities and libraries used by all Hadoop components.
Answer: D. It is a set of common utilities and libraries used by all Hadoop components.
Explanation: Hadoop Common is a set of common utilities and libraries used by all Hadoop components, such as HDFS, MapReduce, and YARN. It provides a set of low-level APIs and utilities for working with files, networking, and other system-level functions.
41. Which of the following is true about Apache Spark?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a high-performance data processing engine for large-scale data processing.
Answer: D. It is a high-performance data processing engine for large-scale data processing.
Explanation: Apache Spark is a high-performance data processing engine for large-scale data processing. It provides a unified API for processing data in batch, interactive, and streaming modes, and supports a variety of programming languages, including Java, Python, and Scala.
42. Which of the following is true about Hadoop Pipes?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a C++ API for writing MapReduce programs.
Answer: D. It is a C++ API for writing MapReduce programs.
Explanation: Hadoop Pipes is a C++ API for writing MapReduce programs. It allows users to write MapReduce programs in C++ and provides a framework for passing data between the Map and Reduce functions.
43. Which of the following is true about Hadoop’s Capacity Scheduler?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a resource scheduler for Hadoop that allocates resources based on pre-defined capacities.
Answer: D. It is a resource scheduler for Hadoop that allocates resources based on pre-defined capacities.
Explanation: Hadoop’s Capacity Scheduler is a resource scheduler for Hadoop that allocates resources based on pre-defined capacities. It allows users to allocate resources to different queues and specify the maximum capacity for each queue.
44. Which of the following is true about Hadoop’s Distributed Cache?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is a mechanism for distributing files, archives, and other resources needed by MapReduce jobs to the task nodes.
Answer: D. It is a mechanism for distributing files, archives, and other resources needed by MapReduce jobs to the task nodes.
Explanation: Hadoop’s Distributed Cache is a mechanism for distributing files, archives, and other resources needed by MapReduce jobs to the task nodes. It allows users to specify files or archives that are needed by the MapReduce job and ensures that they are available on all task nodes before the job starts.
45. Which of the following is true about Hadoop’s InputFormat?
A. It is a distributed data processing framework.
B. It is a programming language for processing data.
C. It is a tool for real-time processing.
D. It is an interface that defines how input data is read and converted into key-value pairs for processing in a MapReduce job.
Answer: D. It is an interface that defines how input data is read and converted into key-value pairs for processing in a MapReduce job.
Explanation: Hadoop’s InputFormat is an interface that defines how input data is read and converted into key-value pairs for processing in a MapReduce job. It provides a framework for reading data from different sources, such as HDFS, local file systems, and databases, and converting it into key-value pairs that can be processed by the MapReduce job.
MapReduce is a powerful tool for processing and analyzing large datasets in a distributed computing environment. Users can enhance their proficiency in using this programming model and leverage its capabilities to gain insights from big data by using Hadoop MapReduce MCQs to test their knowledge and understanding of MapReduce’s features and functionalities. For more technical quizzes on various technical concepts, browse our Freshersnow website.