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CS Free MCQ Bank

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  • Big Data MCQs with Answers (PYQs) | Hadoop, MapReduce | Suraku Academy

Q: 1 Which of the following open source framework manages processing and storing for Big Data application?

  1. Hadoop

  2. Struts

  3. Spring

  4. Ant

[ Option A ]

In Big Data applications, large volumes of data need to be stored and processed efficiently across multiple machines. For this purpose, specialized frameworks are used that support distributed computing.
Hadoop is an open-source framework designed specifically for distributed storage (HDFS) and parallel data processing (MapReduce), making it suitable for Big Data applications.

  • HDFS (Hadoop Distributed File System).
FrameworkTypeDescription
Apache HadoopBig Data FrameworkIt is used to store and process large data using distributed systems.
Apache StrutsWeb FrameworkIt is used to build Java-based web applications using MVC architecture.
Spring FrameworkApplication FrameworkIt is used to develop robust and scalable Java applications.
Apache AntBuild ToolIt is used to compile, build, and manage Java projects automatically.

Q: 2 Which of the following is used for storing unstructured data in Big—Data environment?

  1. In-Memory Database

  2. Relational Database

  3. NoSQL Database

  4. SQL Database

[ Option C ]

In a Big Data environment, data often exists in unstructured or semi-structured forms such as text, images, videos, social media data, and sensor logs. 
NoSQL Databases are designed to handle such data because they provide flexible schemas, horizontal scalability, and distributed storage. Examples include MongoDB, Cassandra, and HBase.

  • Relational Databases (SQL Databases) store data in structured tables with fixed schemas.
  • In-Memory Databases mainly store data in main memory for faster access.

Q: 3 What is the primary purpose of Hadoop Distributed File System (HDFS) in Big Data Storage?

  1. To compress file

  2. To store in-memory data

  3. To store relational data

  4. To store large files across multiple machines

[ Option D ]

The Hadoop Distributed File System (HDFS) is a distributed storage system designed to store very large datasets across multiple machines in a cluster. It divides large files into smaller blocks and distributes them across different nodes, providing fault tolerance, scalability, and high-throughput access to data.

Q: 4 Which of the following is not a characteristics of Big Data?

  1. Velocity

  2. Visualisation

  3. Volume

  4. Variety

[ Option B ]

Big Data is commonly characterized by the 3Vs: Volume, Velocity, and Variety.

  • Volume refers to the large amount of data generated and stored.
  • Velocity refers to the speed at which data is generated, processed, and analyzed.
  • Variety refers to the different types of data, such as structured, semi-structured, and unstructured data.

Visualization is a technique used to present or analyze data graphically, but it is not one of the fundamental characteristics of Big Data.

Q: 5 Which of the following is a Big—Data tools employed for real-time stream processing?

  1. Hadoop

  2. Apache Flink

  3. Splunk

  4. ERDPlus

[ Option B ]

Apache Flink is a Big Data processing framework designed for real-time stream processing and high-throughput data analysis. It processes continuous streams of data with low latency, making it suitable for applications such as real-time analytics, event processing, and monitoring systems.

Q: 6

Match the following in the context of Big—Data:

Group—IGroup—II
(i)   Structured Data(P) Text files, Images, Videos
(ii)  Semi-Structured Data(Q) Fixed Format Data
(iii)  Unstructured Data(R) XML, JSON

  1. (i)—(Q), (ii)—(R), (iii)—(P)

  2. (i)—(Q), (ii)—(P), (iii)—(R)

  3. (i)—(P), (ii)—(R), (iii)—(Q)

  4. (i)—(R), (ii)—(Q), (iii)—(P)

[ Option A ]

In Big Data, data is commonly classified into structured, semi-structured, and unstructured data based on how it is organized and stored. 

  • Structured Data follows a fixed format or predefined schema, usually stored in rows and columns like in relational databases.
  • Semi-Structured Data does not follow a rigid table structure but still contains tags or markers that organize the data, making it partially structured. Common examples include XML and JSON files.
  • Unstructured Data has no predefined format or schema and cannot be easily organized in tables. Examples include text documents, images, audio, and videos.

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