Hadoop MCQ Questions and Answers

Leave a Comment / By /

1. What does Hadoop primarily process?

a) Real-time data
b) Small data sets
c) Structured data only
d) Large data sets

Answer:

d) Large data sets

Explanation:

Hadoop is designed for processing large data sets, handling petabytes and exabytes of data efficiently across distributed clusters.

2. Which component serves as the data warehouse framework in Hadoop?

a) HBase
b) Hive
c) Pig
d) ZooKeeper

Answer:

b) Hive

Explanation:

Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

3. What is MapReduce in Hadoop?

a) A data storage component
b) A programming model for processing large data sets
c) A tool for data transfer
d) A database management system

Answer:

b) A programming model for processing large data sets

Explanation:

MapReduce is a programming model in Hadoop for processing large data sets with a parallel, distributed algorithm on a cluster.

4. What is the role of the JobTracker in Hadoop?

a) It stores metadata for HDFS
b) It coordinates and monitors MapReduce jobs
c) It handles data replication
d) It manages database connections

Answer:

b) It coordinates and monitors MapReduce jobs

Explanation:

The JobTracker in Hadoop is responsible for coordinating and monitoring the execution of MapReduce jobs on the cluster.

5. What type of file system is HDFS?

a) Distributed file system
b) Local file system
c) Network file system
d) Virtual file system

Answer:

a) Distributed file system

Explanation:

HDFS, or Hadoop Distributed File System, is a distributed file system designed to run on commodity hardware and provide high throughput.

6. In Hadoop, what is a 'Node'?

a) A point of data storage
b) A Java object
c) A single data record
d) A computer in the cluster

Answer:

d) A computer in the cluster

Explanation:

In the context of Hadoop, a 'Node' refers to a single computer in the Hadoop cluster. Each node stores part of the data and participates in the cluster's data processing.

7. Which of the following is a column-oriented NoSQL database in Hadoop ecosystem?

a) Cassandra
b) MongoDB
c) HBase
d) Neo4j

Answer:

c) HBase

Explanation:

HBase is a column-oriented NoSQL database in the Hadoop ecosystem, designed for storing sparse data sets.

8. What does YARN stand for in Hadoop?

a) Yielded Architecture for Resource Negotiation
b) Yet Another Resource Negotiator
c) Yarned Array of Networked Nodes
d) Young Architecture for Random Networks

Answer:

b) Yet Another Resource Negotiator

Explanation:

YARN in Hadoop stands for Yet Another Resource Negotiator. It is responsible for managing computing resources in clusters and using them for scheduling user applications.

9. What is the function of the NameNode in Hadoop?

a) It stores the actual data
b) It manages the metadata of the HDFS
c) It executes data processing tasks
d) It balances the load across DataNodes

Answer:

b) It manages the metadata of the HDFS

Explanation:

In Hadoop, the NameNode manages the metadata of the HDFS. It keeps track of the file system tree and the metadata for all the files and directories.

10. Which tool is used for transferring data between Hadoop and relational databases?

a) Flume
b) Sqoop
c) Oozie
d) ZooKeeper

Answer:

b) Sqoop

Explanation:

Sqoop is a tool used in Hadoop for transferring data between the Hadoop ecosystem and relational databases efficiently.

11. What is Pig in Hadoop?

a) A data storage unit
b) A reporting tool
c) A high-level scripting language
d) A type of file system

Answer:

c) A high-level scripting language

Explanation:

Pig is a high-level scripting language in Hadoop used for data analysis and transformation. It abstracts the complexity of writing MapReduce programs.

12. What is the default replication factor in HDFS?

a) 1
b) 2
c) 3
d) 4

Answer:

c) 3

Explanation:

The default replication factor in HDFS is 3, meaning that HDFS creates three copies of each file block for fault tolerance.

13. What kind of data processing does MapReduce perform?

a) Only sequential data processing
b) Only real-time data processing
c) Parallel data processing
d) Only graphical data processing

Answer:

c) Parallel data processing

Explanation:

MapReduce performs parallel data processing, allowing for efficient processing of very large data sets by dividing the work across multiple nodes in a cluster.

14. Which component acts as the brain of the Hadoop ecosystem?

a) DataNode
b) Secondary NameNode
c) NameNode
d) JobTracker

Answer:

c) NameNode

Explanation:

The NameNode is often considered the brain of the Hadoop ecosystem as it manages the HDFS and keeps track of where data resides in the cluster.

15. What is the purpose of Hadoop Oozie?

a) Data storage
b) Data analysis
c) Workflow scheduling and management
d) Real-time processing

Answer:

c) Workflow scheduling and management

Explanation:

Oozie is used in Hadoop for workflow scheduling and management. It allows users to define a series of jobs to be executed in a specified order.

16. Which Hadoop component provides a distributed real-time database?

a) Hive
b) HBase
c) Pig
d) Sqoop

Answer:

b) HBase

Explanation:

HBase provides a distributed real-time database in the Hadoop ecosystem. It is suitable for storing large amounts of sparse data.

17. How does Hadoop achieve scalability?

a) Through vertical scaling
b) By adding more RAM
c) Through horizontal scaling
d) By using faster CPUs

Answer:

c) Through horizontal scaling

Explanation:

Hadoop achieves scalability through horizontal scaling, meaning that it can process more data by adding more nodes to the cluster.

18. What is Hue in the context of Hadoop?

a) A hardware unit
b) A user interface for interacting with Hadoop ecosystem components
c) A scripting language
d) A real-time processing framework

Answer:

b) A user interface for interacting with Hadoop ecosystem components

Explanation:

Hue stands for Hadoop User Experience and is a web-based user interface for interacting with various components of the Hadoop ecosystem, like HDFS, MapReduce, Hive, Pig, and others.

19. What is the primary role of the Resource Manager in YARN?

a) Managing the storage of data
b) Monitoring the health of the cluster
c) Managing the computing resources in the cluster
d) Data encryption and security

Answer:

c) Managing the computing resources in the cluster

Explanation:

The primary role of the Resource Manager in YARN (Yet Another Resource Negotiator) is to manage the computing resources in the cluster, including allocating resources to various running applications.

20. In Hadoop, what is the purpose of a Combiner?

a) To combine multiple data sources
b) To merge output from the Map phase before it's sent to the Reduce phase
c) To combine the results of the Reduce phase
d) To concatenate files in HDFS

Answer:

b) To merge output from the Map phase before it's sent to the Reduce phase

Explanation:

In Hadoop, a Combiner is used to optimize the MapReduce process by merging intermediate Map output locally on each mapper before sending to the Reducer. This reduces the amount of data transferred across the network.

21. What does the Hadoop command hdfs dfs -put do?

a) Retrieves a file from HDFS
b) Lists files in HDFS
c) Copies a file from the local file system to HDFS
d) Deletes a file from HDFS

Answer:

c) Copies a file from the local file system to HDFS

Explanation:

The command hdfs dfs -put is used to copy a file from the local file system to HDFS. It's a common command for populating HDFS with data.

22. What type of software is Apache Hadoop?

a) Commercial software
b) Open-source software
c) Shareware
d) Proprietary software

Answer:

b) Open-source software

Explanation:

Apache Hadoop is an open-source software framework. It is freely available and can be modified and redistributed under the Apache License.

23. What is the function of the Secondary NameNode in Hadoop?

a) It serves as a backup to the NameNode
b) It takes over the role of the NameNode in case of failure
c) It performs housekeeping tasks for the NameNode
d) It is a data node that stores the actual data

Answer:

c) It performs housekeeping tasks for the NameNode

Explanation:

The Secondary NameNode in Hadoop performs housekeeping tasks for the NameNode, like merging the fsimage and the edit logs, but it doesn't serve as a backup or replace the NameNode in case of failure.

24. In Hadoop, what does 'Rack Awareness' refer to?

a) Awareness of the physical location of nodes
b) The status of server racks in the cluster
c) The software awareness of hardware components
d) Load balancing across racks

Answer:

a) Awareness of the physical location of nodes

Explanation:

'Rack Awareness' in Hadoop refers to the knowledge of the cluster about the physical location (rack) of nodes. It helps in optimizing data replication and reducing network traffic by considering the rack and node topology in data placement.

25. How does Hadoop ensure data privacy and security?

a) By default, Hadoop does not provide any data privacy or security features
b) Using built-in encryption and authentication mechanisms
c) Through third-party security applications
d) By storing data in encrypted blocks

Answer:

b) Using built-in encryption and authentication mechanisms

Explanation:

Hadoop ensures data privacy and security using built-in features such as Kerberos authentication, encryption in HDFS, and authorization via Access Control Lists (ACLs) and Apache Ranger or Apache Sentry.

Related MCQ (Multiple Choice Questions) :

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top