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Hadoop MCQs and Answers With Explanation – If you’re looking to test your knowledge of Hadoop and learn more about its concepts, this article on Hadoop Multiple Choice Questions and Answers is a great resource. The accompanying Hadoop MCQ Quiz/ Hadoop MCQs can help you evaluate your understanding of the framework. To begin, let’s review the basics of Hadoop: it’s an open-source platform that enables distributed storage and processing of large datasets. Developed by the Apache Software Foundation, Hadoop is widely used for big data analytics and processing. It provides a scalable and fault-tolerant platform that allows for the distributed processing of massive datasets across clusters of commodity hardware.
Hadoop MCQs
With its flexible and versatile architecture, Hadoop has become an essential tool for a wide range of big data applications, including data warehousing, ETL (Extract, Transform, Load) operations, and real-time analytics. Now by following this article you can gain access to the Top 60 Hadoop Multiple Choice Questions and Answers which will be helpful for your in gaining complete knowledge about Hadoop.
Hadoop Multiple Choice Questions
| Name | Hadoop |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 60 Hadoop MCQs With Answers | Practice Online Quiz
1. What is Hadoop?
A. A software framework for distributed storage and processing of large data sets
B. A programming language
C. A database management system
D. A data visualization tool
Answer: A. Hadoop is a software framework for distributed storage and processing of large data sets.
Explanation: Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
2. What is the main advantage of using Hadoop?
A. It can handle large amounts of structured data
B. It is easy to set up and configure
C. It is faster than other big data solutions
D. It can handle large amounts of unstructured data
Answer: D. Hadoop can handle large amounts of unstructured data.
Explanation: Hadoop is designed to handle large amounts of unstructured data, which is data that doesn’t fit neatly into a table or database. This makes it ideal for processing things like social media data, web logs, and other types of data that can’t be easily analyzed using traditional database technologies.
3. What is a Hadoop cluster?
A. A group of computers that work together to store and process data
B. A tool for creating data visualizations
C. A programming language used for big data analysis
D. A type of database management system
Answer: A. A Hadoop cluster is a group of computers that work together to store and process data.
Explanation: In a Hadoop cluster, multiple computers work together to store and process large amounts of data. This allows for much faster processing than would be possible with a single computer.
4. What is a NameNode in Hadoop?
A. The primary node in a Hadoop cluster
B. A tool for visualizing Hadoop data
C. A programming language used in Hadoop
D. A database management system used in Hadoop
Answer: A. The NameNode is the primary node in a Hadoop cluster.
Explanation: The NameNode is responsible for managing the file system metadata in a Hadoop cluster. This includes information about where files are stored in the cluster and how they are divided across the different nodes.
5. What is a DataNode in Hadoop?
A. A node in a Hadoop cluster that stores data
B. A tool for visualizing Hadoop data
C. A programming language used in Hadoop
D. A database management system used in Hadoop
Answer: A. A DataNode is a node in a Hadoop cluster that stores data.
Explanation: In a Hadoop cluster, the DataNodes are responsible for storing the actual data. They receive instructions from the NameNode about where to store the data and how to replicate it across the cluster.
6. What is HDFS?
A. Hadoop Distributed File System
B. Hadoop Data Flow System
C. Hadoop Data Fragmentation System
D. Hadoop Data Filtering System
Answer: A. HDFS stands for Hadoop Distributed File System.
Explanation: HDFS is the primary storage system used in Hadoop. It is designed to store large files across multiple machines in a Hadoop cluster.
7. What is MapReduce in Hadoop?
A. A programming model for processing large data sets
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. MapReduce is a programming model for processing large data sets.
Explanation: MapReduce is a programming model that allows developers to write programs that can process large data sets in parallel across a Hadoop cluster. It is the primary processing framework used in Hadoop.
8. What is a task tracker in Hadoop?
A. A daemon process that runs on a node in a Hadoop cluster and executes MapReduce tasks
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A task tracker is a daemon process that runs on a node in a Hadoop cluster and executes MapReduce tasks.
Explanation: Task trackers are responsible for running individual MapReduce tasks on nodes in a Hadoop cluster. They receive instructions from the job tracker and execute the tasks, reporting their progress back to the job tracker.
9. What is the purpose of the shuffle phase in MapReduce?
A. To group together all intermediate values with the same key
B. To sort the intermediate values by key
C. To combine the intermediate values into a single output
D. To send the intermediate values to the reducers
Answer: B. The purpose of the shuffle phase in MapReduce is to sort the intermediate values by key.
Explanation: In the shuffle phase, the intermediate values produced by the map tasks are sorted by key so that they can be grouped together by key in the reduce tasks.
10. What is the purpose of the reduce phase in MapReduce?
A. To group together all intermediate values with the same key
B. To sort the intermediate values by key
C. To combine the intermediate values into a single output
D. To send the intermediate values to the reducers
Answer: C. The purpose of the reduce phase in MapReduce is to combine the intermediate values into a single output.
Explanation: In the reduce phase, the intermediate values produced by the map tasks are grouped together by key and processed to produce a single output value for each key.
11. What is the default input format in Hadoop?
A. Text Input Format
B. SequenceFile Input Format
C. XML Input Format
D. Avro Input Format
Answer: A. The default input format in Hadoop is the Text Input Format.
Explanation: The Text Input Format is the default input format in Hadoop. It is used to read text files stored in HDFS.
12. What is the default output format in Hadoop?
A. Text Output Format
B. SequenceFile Output Format
C. XML Output Format
D. Avro Output Format
Answer: A. The default output format in Hadoop is the Text Output Format.
Explanation: The Text Output Format is the default output format in Hadoop. It is used to write text files to HDFS.
13. What is a combiner in Hadoop?
A. A function that can be used to perform partial aggregation on the output of map tasks
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A combiner is a function that can be used to perform partial aggregation on the output of map tasks.
Explanation: Combiners are used to perform partial aggregation on the output of map tasks before the data is sent to the reducers. This can help to reduce the amount of data that needs to be processed by the reducers.
14. What is a partitioner in Hadoop?
A. A function that determines the mapping of key-value pairs to reducers
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A partitioner is a function that determines the mapping of key-value pairs to reducers.
Explanation: Partitioners are used to determine which reducer will process each key-value pair produced by the map tasks. They typically use the key of the pair to make this determination.
15. What is speculative execution in Hadoop?
A. The ability to launch multiple copies of a task to run in parallel
B. The ability to run a task on multiple nodes simultaneously
C. The ability to recover from a node failure by restarting a task on another node
D. The ability to run a task at a lower priority to avoid impacting other jobs
Answer: A. Speculative execution in Hadoop refers to the ability to launch multiple copies of a task to run in parallel.
Explanation: When speculative execution is enabled, Hadoop will launch multiple copies of a task on different nodes in the cluster. The first copy to complete successfully will be used, and the others will be killed.
16. What is a block in HDFS?
A. A unit of data storage in HDFS
B. A data structure used in MapReduce
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A block is a unit of data storage in HDFS.
Explanation: In HDFS, data is divided into blocks and distributed across the nodes in the cluster. Each block is typically 64MB or 128MB in size.
17. What is a NameNode in HDFS?
A. A daemon process that manages the metadata of files stored in HDFS
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A NameNode is a daemon process that manages the metadata of files stored in HDFS.
Explanation: The NameNode is responsible for maintaining the metadata of files stored in HDFS, including information such as the location of each block of data.
18. What is a DataNode in HDFS?
A. A daemon process that stores data blocks in HDFS
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A DataNode is a daemon process that stores data blocks in HDFS.
Explanation: DataNodes are responsible for storing the actual data blocks that make up files stored in HDFS.
19. How does Hadoop ensure data reliability in HDFS?
A. By replicating each data block to multiple nodes in the cluster
B. By compressing the data before it is stored in HDFS
C. By encrypting the data before it is stored in HDFS
D. By distributing the data across multiple data centers
Answer: A. Hadoop ensures data reliability in HDFS by replicating each data block to multiple nodes in the cluster.
Explanation: When a file is stored in HDFS, each block of data is typically replicated to multiple nodes in the cluster to ensure that there are multiple copies of the data available in case of node failure.
20. What is a secondary NameNode in Hadoop?
A. A daemon process that periodically merges the edit log and fsimage files in HDFS
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A secondary NameNode is a daemon process that periodically merges the edit log and fsimage files in HDFS.
Explanation: The secondary NameNode is responsible for periodically merging the edit log and fsimage files in HDFS to create a new fsimage file. This process helps to prevent the NameNode from becoming overloaded with metadata operations.
21. What is the purpose of a Combiner in Hadoop?
A. To aggregate intermediate data before sending it to reducers
B. To sort intermediate data before sending it to reducers
C. To partition intermediate data before sending it to reducers
D. To perform calculations on intermediate data before sending it to reducers
Answer: A. The purpose of a Combiner in Hadoop is to aggregate intermediate data before sending it to reducers.
Explanation: Combiners are used to perform partial aggregation on the intermediate data produced by the map tasks. This reduces the amount of data that needs to be transferred to the reducers, which can improve performance.
22. What is a reducer in Hadoop?
A. A task that processes intermediate key-value pairs generated by the map tasks
B. A tool for visualizing Hadoop data
C. A database management system used in Hadoop
D. A file system used in Hadoop
Answer: A. A reducer in Hadoop is a task that processes intermediate key-value pairs generated by the map tasks.
Explanation: Reducers are responsible for processing the intermediate data produced by the map tasks. They receive input in the form of key-value pairs, and they output a new set of key-value pairs that are written to HDFS.
23. What is the purpose of a partitioner in Hadoop?
A. To determine the mapping of key-value pairs to reducers
B. To aggregate intermediate data before sending it to reducers
C. To sort intermediate data before sending it to reducers
D. To perform calculations on intermediate data before sending it to reducers
Answer: A. The purpose of a partitioner in Hadoop is to determine the mapping of key-value pairs to reducers.
Explanation: Partitioners are used to determine which reducer will process each key-value pair produced by the map tasks. They typically use the key of the pair to make this determination.
24. What is the purpose of the JobTracker in Hadoop?
A. To manage the execution of MapReduce jobs in Hadoop
B. To manage the metadata of files stored in HDFS
C. To store and manage data in Hadoop
D. To provide a user interface for Hadoop
Answer: A. The purpose of the JobTracker in Hadoop is to manage the execution of MapReduce jobs.
Explanation: The JobTracker is responsible for coordinating the execution of MapReduce jobs in Hadoop. It schedules tasks to run on the available TaskTrackers, and it monitors the progress of each task.
25. What is the purpose of the TaskTracker in Hadoop?
A. To execute tasks assigned by the JobTracker
B. To manage the metadata of files stored in HDFS
C. To store and manage data in Hadoop
D. To provide a user interface for Hadoop
Answer: A. The purpose of the TaskTracker in Hadoop is to execute tasks assigned by the JobTracker.
Explanation: TaskTrackers are responsible for executing tasks assigned by the JobTracker. They communicate with the JobTracker to receive task assignments and to report their progress. They also manage the local storage and network bandwidth used by the tasks.
26. Which of the following is not a component of Hadoop?
A. HBase
B. Pig
C. Hive
D. Spark
Answer: D. Spark is not a component of Hadoop.
Explanation: Spark is a separate open-source data processing framework that can be used with Hadoop. It is often used as an alternative to MapReduce for processing large datasets.
27. What is the purpose of HBase in Hadoop?
A. To provide real-time access to large datasets
B. To store and manage large amounts of unstructured data
C. To provide a user interface for Hadoop
D. To manage the metadata of files stored in HDFS
Answer: A. The purpose of HBase in Hadoop is to provide real-time access to large datasets.
Explanation: HBase is a NoSQL database that runs on top of Hadoop. It is designed to provide real-time access to large datasets, and it can store and manage large amounts of unstructured data.
28. What is the purpose of Pig in Hadoop?
A. To process and analyze large datasets
B. To store and manage large amounts of unstructured data
C. To provide a user interface for Hadoop
D. To provide real-time access to large datasets
Answer: A. The purpose of Pig in Hadoop is to process and analyze large datasets.
Explanation: Pig is a high-level scripting language that is used to process and analyze large datasets in Hadoop. It provides a simplified syntax for writing MapReduce programs, which can help to speed up development and reduce errors.
29. What is the purpose of Hive in Hadoop?
A. To provide a SQL-like interface for querying data in Hadoop
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of Hive in Hadoop is to provide a SQL-like interface for querying data in Hadoop.
Explanation: Hive is a data warehousing framework that provides a SQL-like interface for querying data in Hadoop. It allows users to write SQL queries that are translated into MapReduce programs, which can be used to process large amounts of data.
30. What is the purpose of Sqoop in Hadoop?
A. To transfer data between Hadoop and external data sources
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of Sqoop in Hadoop is to transfer data between Hadoop and external data sources.
Explanation: Sqoop is a tool that is used to transfer data between Hadoop and external data sources, such as relational databases. It allows users to import data from these sources into Hadoop, and it can also be used to export data from Hadoop back to these sources.
31. What is the purpose of Flume in Hadoop?
A. To collect and aggregate large amounts of log data
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of Flume in Hadoop is to collect and aggregate large amounts of log data.
Explanation: Flume is a distributed system for collecting, aggregating, and moving large amounts of log data from various sources into Hadoop for storage and analysis.
32. What is the purpose of Oozie in Hadoop?
A. To manage and schedule workflows of Hadoop jobs
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of Oozie in Hadoop is to manage and schedule workflows of Hadoop jobs.
Explanation: Oozie is a workflow scheduler system for managing and scheduling Hadoop jobs. It allows users to create and manage workflows that consist of multiple Hadoop jobs, and it can be used to automate complex data processing tasks.
33. What is the purpose of Mahout in Hadoop?
A. To provide a machine learning library for Hadoop
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of Mahout in Hadoop is to provide a machine learning library for Hadoop.
Explanation: Mahout is a machine learning library that provides a variety of algorithms for processing and analyzing large datasets in Hadoop. It can be used for tasks such as classification, clustering, and collaborative filtering.
34. What is the purpose of ZooKeeper in Hadoop?
A. To manage and coordinate distributed systems
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of ZooKeeper in Hadoop is to manage and coordinate distributed systems.
Explanation: ZooKeeper is a distributed coordination service that is used to manage and coordinate distributed systems, such as those used in Hadoop. It provides a centralized repository for configuration information and can be used to maintain state information for distributed systems.
35. What is the purpose of Ambari in Hadoop?
A. To manage and monitor Hadoop clusters
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of Ambari in Hadoop is to manage and monitor Hadoop clusters.
Explanation: Ambari is a web-based tool for managing and monitoring Hadoop clusters. It provides a centralized interface for configuring, managing, and monitoring Hadoop services and components.
36. What is the purpose of YARN in Hadoop?
A. To manage resources and schedule jobs in Hadoop
B. To store and manage large amounts of unstructured data
C. To provide real-time access to large datasets
D. To process and analyze large datasets
Answer: A. The purpose of YARN in Hadoop is to manage resources and schedule jobs in Hadoop.
Explanation: YARN (Yet Another Resource Negotiator) is a component of Hadoop that is responsible for managing resources and scheduling jobs. It allows multiple data processing engines, such as MapReduce and Spark, to run on the same cluster and share resources.
37. Which of the following is not a characteristic of Big Data?
A. Volume
B. Velocity
C. Variety
D. Validation
Answer: D. Validation is not a characteristic of Big Data.
Explanation: The characteristics of Big Data are commonly referred to as the “3Vs”: Volume, Velocity, and Variety. Validation is not typically considered a characteristic of Big Data, although it is an important aspect of data quality.
38. Which of the following is not a common use case for Hadoop?
A. Log processing and analysis
B. E-commerce personalization
C. Social media analysis
D. Desktop publishing
Answer: D. Desktop publishing is not a common use case for Hadoop.
Explanation: Hadoop is typically used for processing and analyzing large datasets, particularly those that are too large to be handled by traditional data processing tools. Common use cases include log processing and analysis, e-commerce personalization, social media analysis, and many more. However, desktop publishing is not a typical use case for Hadoop.
39. Which Hadoop component is responsible for storing data on disk?
A. HDFS
B. MapReduce
C. YARN
D. Hive
Answer: A. HDFS is the Hadoop component responsible for storing data on disk.
Explanation: HDFS (Hadoop Distributed File System) is the primary storage system used in Hadoop. It is responsible for storing and retrieving data from disk, and it provides a fault-tolerant and scalable storage solution for large datasets.
40. Which Hadoop component is responsible for processing data?
A. HDFS
B. MapReduce
C. YARN
D. Hive
Answer: B. MapReduce is the Hadoop component responsible for processing data.
Explanation: MapReduce is a programming model used for processing large datasets in Hadoop. It provides a way to parallelize data processing tasks across multiple nodes in a Hadoop cluster, and it is typically used for tasks such as data filtering, sorting, and aggregation.
41. Which Hadoop component is responsible for managing resources and scheduling jobs?
A. HDFS
B. MapReduce
C. YARN
D. Hive
Answer: C. YARN (Yet Another Resource Negotiator) is the Hadoop component responsible for managing resources and scheduling jobs.
Explanation: YARN is a component of Hadoop that is responsible for managing resources and scheduling jobs. It allows multiple data processing engines, such as MapReduce and Spark, to run on the same cluster and share resources.
42. Which Hadoop component is used for data warehousing?
A. HDFS
B. MapReduce
C. YARN
D. Hive
Answer: D. Hive is the Hadoop component used for data warehousing.
Explanation: Hive is a data warehousing tool that provides a SQL-like interface for querying and analyzing data stored in Hadoop. It allows users to write queries in a familiar SQL syntax and translates them into MapReduce jobs that can be executed on a Hadoop cluster.
43. Which Hadoop component is used for real-time stream processing?
A. HDFS
B. MapReduce
C. YARN
D. Spark Streaming
Answer: D. Spark Streaming is the Hadoop component used for real-time stream processing.
Explanation: Spark Streaming is a component of Apache Spark that is used for processing real-time data streams. It allows users to process data streams in near real-time and provides a way to integrate real-time processing with batch processing in Hadoop.
44. Which of the following is not a type of Hadoop cluster?
A. Standalone
B. Pseudo-distributed
C. Fully-distributed
D. Hybrid
Answer: D. Hybrid is not a type of Hadoop cluster.
Explanation: Standalone, pseudo-distributed, and fully-distributed are the three main types of Hadoop clusters. A standalone cluster is used for testing and development, while a pseudo-distributed cluster runs on a single machine and simulates a distributed cluster. A fully-distributed cluster is a true distributed cluster that runs on multiple machines. A hybrid cluster is not a standard type of Hadoop cluster.
45. Which Hadoop component provides a SQL-like interface for querying data?
A. HDFS
B. MapReduce
C. YARN
D. Hive
Answer: D. Hive provides a SQL-like interface for querying data.
Explanation: Hive is a data warehousing tool that provides a SQL-like interface for querying and analyzing data stored in Hadoop. It allows users to write queries in a familiar SQL syntax and translates them into MapReduce jobs that can be executed on a Hadoop cluster.
46. Which Hadoop component provides a real-time stream processing engine?
A. HDFS
B. MapReduce
C. YARN
D. Spark Streaming
Answer: D. Spark Streaming provides a real-time stream processing engine in Hadoop.
Explanation: Spark Streaming is a component of Apache Spark that is used for processing real-time data streams. It allows users to process data streams in near real-time and provides a way to integrate real-time processing with batch processing in Hadoop.
47. Which Hadoop component is responsible for managing metadata?
A. HDFS
B. MapReduce
C. YARN
D. ZooKeeper
Answer: D. ZooKeeper is the Hadoop component responsible for managing metadata.
Explanation: ZooKeeper is a centralized service used for maintaining configuration information, naming, synchronization, and providing group services. In Hadoop, ZooKeeper is used for managing metadata, such as the location of data blocks in HDFS, and for coordinating distributed applications.
48. Which Hadoop component is used for data processing in real-time?
A. HDFS
B. MapReduce
C. YARN
D. Spark
Answer: D. Spark is the Hadoop component used for data processing in real-time.
Explanation: Spark is an open-source, distributed computing system used for processing large datasets in real-time. It provides an interface for programming clusters with implicit data parallelism and fault tolerance. Spark can run on top of Hadoop, and it can also be used in standalone mode.
49. Which Hadoop component is responsible for data ingestion?
A. HDFS
B. MapReduce
C. YARN
D. Flume
Answer: D. Flume is the Hadoop component responsible for data ingestion.
Explanation: Flume is a distributed, reliable, and available service used for efficiently collecting, aggregating, and moving large amounts of log data from many different sources to a centralized data store in Hadoop. It is designed to handle a large number of data sources and to scale horizontally as the number of sources and the amount of data grows.
50. Which Hadoop component is used for data processing with Python?
A. HDFS
B. MapReduce
C. YARN
D. PySpark
Answer: D. PySpark is the Hadoop component used for data processing with Python.
Explanation: PySpark is the Python API for Apache Spark. It provides an interface for programming clusters with implicit data parallelism and fault tolerance using Python. PySpark can run on top of Hadoop, and it can also be used in standalone mode.
51. Which Hadoop component is responsible for creating indexes on data stored in Hadoop?
A. HBase
B. MapReduce
C. YARN
D. Pig
Answer: A. HBase is the Hadoop component responsible for creating indexes on data stored in Hadoop.
Explanation: HBase is a distributed, column-oriented database built on top of Hadoop’s HDFS. It provides random access and strong consistency for large datasets. HBase is designed to handle structured data and provides mechanisms for creating indexes on data stored in Hadoop.
52. Which Hadoop component is used for interactive data analysis?
A. HDFS
B. MapReduce
C. YARN
D. Impala
Answer: D. Impala is the Hadoop component used for interactive data analysis.
Explanation: Impala is an open-source, distributed SQL query engine used for processing large datasets in Hadoop. It provides an interface for performing real-time interactive queries on data stored in Hadoop, and it supports a variety of SQL operations and data types.
53. Which Hadoop component is responsible for creating data pipelines?
A. HDFS
B. MapReduce
C. YARN
D. Oozie
Answer: D. Oozie is the Hadoop component responsible for creating data pipelines.
Explanation: Oozie is a workflow scheduler system used for managing Apache Hadoop jobs. It provides a way to create and manage data pipelines, and it supports various Hadoop components, such as MapReduce, Pig, Hive, and Sqoop.
54. Which Hadoop component is used for SQL-based data processing?
A. HDFS
B. MapReduce
C. YARN
D. Hive
Answer: D. Hive is the Hadoop component used for SQL-based data processing.
Explanation: Hive is a data warehousing and SQL-based data processing tool used for querying and analyzing large datasets stored in Hadoop’s HDFS. It provides an SQL-like interface for users to run queries, and it converts SQL queries into MapReduce or Tez jobs for processing.
55. Which Hadoop component is responsible for data serialization?
A. HDFS
B. MapReduce
C. YARN
D. Avro
Answer: D. Avro is the Hadoop component responsible for data serialization.
Explanation: Avro is a data serialization system used for exchanging data between Hadoop components. It provides a compact, fast, binary data format that can be used for efficient data exchange between Hadoop components.
56. Which Hadoop component is used for managing resource utilization in Hadoop clusters?
A. HDFS
B. MapReduce
C. YARN
D. HBase
Answer: C. YARN is the Hadoop component used for managing resource utilization in Hadoop clusters.
Explanation: YARN (Yet Another Resource Negotiator) is the resource management layer of Hadoop. It provides a way to manage resources in Hadoop clusters and to allocate resources to applications running on the cluster. YARN allows multiple data processing engines, such as MapReduce, Spark, and Tez, to run on the same cluster, and it provides a way to allocate resources dynamically based on the needs of each application.
57. Which Hadoop component is responsible for data synchronization between Hadoop clusters?
A. HDFS
B. MapReduce
C. YARN
D. DistCp
Answer: D. DistCp is the Hadoop component responsible for data synchronization between Hadoop clusters.
Explanation: DistCp (Distributed Copy) is a tool used for copying large amounts of data between Hadoop clusters. It is designed to work with Hadoop’s HDFS and supports copying of data across different Hadoop clusters and different versions of Hadoop.
58. Which Hadoop component is used for graph processing?
A. HDFS
B. MapReduce
C. YARN
D. Giraph
Answer: D. Giraph is the Hadoop component used for graph processing.
Explanation: Giraph is an open-source, distributed graph processing system built on top of Hadoop’s HDFS and YARN. It provides an interface for processing large-scale graphs and supports a variety of graph algorithms, such as PageRank, shortest path, and connected components.
59. Which Hadoop component is used for machine learning?
A. HDFS
B. MapReduce
C. YARN
D. Mahout
Answer: D. Mahout is the Hadoop component used for machine learning.
Explanation: Mahout is an open-source, distributed machine learning library built on top of Hadoop’s MapReduce and HDFS. It provides an interface for running machine learning algorithms on large datasets and supports a variety of machine learning tasks, such as clustering, classification, and recommendation.
60. Which Hadoop component is used for data exploration and visualization?
A. HDFS
B. MapReduce
C. YARN
D. Zeppelin
Answer: D. Zeppelin is the Hadoop component used for data exploration and visualization.
Explanation: Zeppelin is a web-based notebook interface for data exploration, visualization, and collaboration. It provides an interactive environment for users to explore data and create visualizations using various data sources, including Hadoop’s HDFS, Hive, and Spark.
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