AKTU · B.Tech · Important questions
AKTU Big Data Important Questions
The questions AKTU asks again and again in Big Data, found by comparing every question in 12 previous year papers from 2016-17 to 2025-26 (subject codes BCS061, BOE099, KCS061, KOE097, NIT067).
How this list is made: we compared all 287 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.
Most repeated long questions
From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.
- Illustrate the architecture of Map-Reduce.
- Explain Map Reduce framework in details.
- Explain the detailed architecture of Map-Reduce
- Discuss the detailed architecture of Map-Reduce.
- Demonstrate the design of HDFS and concept in detail.
- Discuss Master Slave and Peer-Peer replication in detail.
- Explain Master slave and peer-peer replication in detail.
- Explain the Anatomy of MapReduce job run.
- What is the anatomy of a Map Reduce job run?
- Illustrate the anatomy of a MapReduce job run.
- Give the Anatomy of a Map Reduce job run in Map Reduce.
- Detail about the analysis vs. reporting while introducing the Big Data
- Give the detailed description about the Analysis vs. reporting while Introducing the Big Data.
- Differentiate “Scale up and Scale out” Explain with an example How Hadoop uses Scale out feature to improve the Performance.
- Differentiate between ‘scale-up’ and ‘scale-out’ approaches in computing systems. Explain in detail how Hadoop uses the scale-out approach to improve system performance, reliability, and fault tolerance, with suitable examples and diagrams.
- Discuss the 5 Vs used in Big Data with the help of an example.
- Discuss the 5 Vs of Big Data and their implications.
- Explain the 5 Vs of Big Data. Also discuss their importance in the context of Big Data?
- Discuss the design of Hadoop Distributed File System (HDFS) in detail.
- Explain the architecture of Hadoop Distributed File System (HDFS) and its fault tolerance mechanisms.
- Describe the Hadoop Distributed File System (HDFS). How does HDFS manage the storage and replication of data across a distributed cluster of machines?
- Elaborate various components of Big Data architecture.
- Explain how CRUD operations with example are performed in MongoDB.
- With the help of suitable example, explain how CRUD operations are performed in MongoDB.
- Mention about the Cluster specification? Describe how to Setting up a Hadoop Cluster?
- Discuss in brief about the cluster specification. Describe how to setting up a Hadoop Cluster?
- What is Hadoop Ecosystem? Explain various components of Hadoop Ecosystem.
- Explain Hadoop ecosystem in detail.
- Compare and Contrast (i) Apache Pig vs Map-Reduce (ii) Pig vs SQL (iii) Pig vs HIVE
- Describe Structured, Semi structured and Unstructured data? Explain with example.
- Differentiate between structured, semi-structured and unstructured data with suitable example.
- Detail about the three dimensions of BIG data.
- Discuss about the three dimensions of Big Data.
- Discuss the detailed architecture of YARN along with its components.
- Examine how a client read and write data in HDFS.
- Explain Spark components in details. Also list features of Spark.
- Explain the components of features and components of Apache Spark.
- Explain the architecture of HIVE. Also explain data flow in HIVE.
- Explain the basics of NoSQL databases and the MongoDB database in particular, including data types, document creation and manipulation, querying, and indexing.
- Explain NoSQL databases in detail and compare them with relational databases. Also, describe the basics of MongoDB including data types, document creation, querying, and indexing.
- Explain the key components of the Hadoop ecosystem and describe how they work together to enable large-scale data storage, processing, and analysis.
- Explain Apache Hadoop and its role in big data processing. What are the core components of the Apache Hadoop ecosystem, and how do they work together to enable distributed data storage and processing?
- Provide overview of HBase data model.
- Write in detail about Hbase data model and pig data model.
- State features of Apache Spark and also explain three ways of how Spark can be built with Hadoop components.
- Write the benefits and challenges of HDFS
- Explain the concept of data replication in HDFS and its benefits and challenges.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- List any five big data platforms.
- What is Big Data?
- Define the term Big Data.
- Explain four V’s of big data
- Explain Hadoop distributed file system.
- Define Hadoop Distributed File System (HDFS).
- Define data replication in Hadoop Distributed File System.
- What do you mean by serialization in Hadoop Distributed File System?
- Compare and Contrast No SQL Relational Databases.
- Compare and Contrast No SQL and Relational Databases
- Differentiate between structured, semi-structured and unstructured data.
- What are the different types of digital data commonly encountered in Big Data applications? Provide examples of structured, semi-structured, and unstructured data.
- Discus the importance of Hadoop technology in Big Data Analytics.
- What is big data analytics?
- Explain characteristics of big data
- List the characteristics of big data.
- L ist the key characteristics of Big Data .
- Define heartbeat in HDFS.
- Differentiate between Map & Reduce .
- Compare the classic Map Reduce with YARN.
- Explain three benefits of MapReduce.
- List the advantages of scala over java.
- Discuss the advantages of scala over java.
- What do you mean by Apache Hadoop?
- How does Apache Hadoop help process the data?
- What is Hadoop Streaming?
- What is YARN?
- What is the role of YARN in Hadoop?
- What is Zookeeper?
- What is the role of Zookeeper in Hadoop?
- What is the block size of a HDFS?
- What is block size in HDFS and why is it important?
- Write any two industry examples for Big Data.
- Write down any four industry examples for Big Data.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BOE099, 2025-26 BCS061, 2024-25 KOE097), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Explain the challenges of Big Data such as storage, scalability, and data quality. Suggest suitable solutions using modern technologies. (2025-26, BCS061)
- A global e-commerce platform generates high-volume transactional data, real-time clickstreams, and social media feedback. Analyze this scenario using the 5 Vs of Big Data and propose a complete Big Data architecture. Justify your choice of tools and technologies. (2025-26, BCS061)
- Explain the architecture of Big Data systems in detail, describing various components, and discuss how these components interact with each other. (2025-26, BOE099)
- Discuss data privacy, security, compliance, and ethics in Big Data. Highlight major challenges and solutions. (2025-26, BOE099)
- What is map reduce? Explain working of various phase of map reduce with appropriate example and diagram. (2024-25, KOE097)
- Explain working of Hive with proper steps and diagram (2024-25, KOE097)
Unit 2
- Differentiate between ‘scale-up’ and ‘scale-out’ approaches in computing systems. Explain in detail how Hadoop uses the scale-out approach to improve system performance, reliability, and fault tolerance, with suitable examples and diagrams. (2025-26, BCS061)
- Describe how a MapReduce job is executed in Hadoop. Explain the roles of job scheduling, task execution, and data flow during processing. (2025-26, BCS061)
- What is Hadoop Ecosystem? Explain various components of Hadoop Ecosystem. (2025-26, BOE099)
- Explain the concept of data locality in Hadoop and discuss how it improves performance by minimizing network congestion and enhancing processing efficiency. (2025-26, BOE099)
- Explain “Map Phase” and “Combiner Phase” in Map Reduce. (2024-25, KOE097)
- What is data serialization? Make a note on how type of data affects data serialization. (2024-25, KOE097)
Unit 3
- Explain how data is written to HDFS. Describe each step involved in storing a file, including block creation and replication. (2025-26, BCS061)
- Explain the role of Flume and Sqoop in Hadoop. Describe how these tools are used for data ingestion from different sources into HDFS. Hadoop Flume Sqoop (2025-26, BCS061)
- Discuss the replication mechanism in HDFS. How the system ensures fault tolerance and data reliability in case of node failures? (2025-26, BOE099)
- Explain a scenario of node failure in HDFS during data write operations. Describe how the system recovers from failures while ensuring no data loss and maintaining consistency. (2025-26, BOE099)
- What is Hadoop Ecosystem? Explain various components of Hadoop Ecosystem. (2024-25, KOE097)
- Explain Spark components in details. Also list features of Spark. (2024-25, KOE097)
Unit 4
- Describe the working of MongoDB. Explain how documents are stored, queried, and indexed in MongoDB. (2025-26, BCS061)
- Explain the architecture of Apache Spark. Describe the concepts of Resilient Distributed Databases (RDDs), jobs, stages, and tasks in data processing. Apache Spark Resilient (2025-26, BCS061)
- Explain NoSQL databases in detail and compare them with relational databases. Also, describe the basics of MongoDB including data types, document creation, querying, and indexing. (2025-26, BOE099)
- Explain basic concepts of Scala programming, including classes, objects, and functional programming features, and discuss its importance in Spark development. (2025-26, BOE099)
- What is NoSQL database? List the differences between NoSQL and relational databases. Explain in brief various types of NoSQL databases in practice. (2024-25, KOE097)
- Define HDFS. Discuss HDFS architecture and HDFS commands in brief. (2024-25, KOE097)
Unit 5
- Compare HBase with relational databases. Explain the advantages of HBase in handling large-scale and real-time data. (2025-26, BCS061)
- A large dataset needs to be processed and analyzed using Hive or Pig. Explain how you would perform data loading, querying, and analysis, and describe how execution happens internally. (2025-26, BCS061)
- Compare HBase with traditional relational databases, focusing on data model, consistency, scalability, and performance. Explain when HBase is preferred over RDBMS. (2025-26, BOE099)
- Discuss the role of Zookeeper in the Hadoop ecosystem, explaining how it helps in coordination, synchronization, and maintaining configuration information in distributed systems (2025-26, BOE099)
- What do you mean by HiveQL data definition language? Explain any three HiveQL data definition language with their syntax and example. (2024-25, KOE097)
- Explain job Scheduling in Map Reduce. How is it done in case of (i) fair scheduling (ii) capacity scheduling (2024-25, KOE097)
Papers used for this list
- AKTU Big Data (BCS061) question paper 2025-26
- AKTU Big Data (BOE099) question paper 2025-26
- AKTU Big Data (BCS061) question paper 2024-25
- AKTU Big Data (KOE097) question paper 2024-25
- AKTU Big Data (KCS061) question paper 2023-24
- AKTU Big Data (KOE097) question paper 2023-24
- AKTU Big Data (KCS061) question paper 2022-23
- AKTU Big Data (KOE097) question paper 2022-20
- AKTU Big Data (KCS061) question paper 2021-22
- AKTU Big Data (KOE097) question paper 2021-22
- AKTU Big Data (NIT067) question paper 2017-18
- AKTU Big Data (NIT067) question paper 2016-17
How to use these questions
- Start with the questions asked in the most papers, and make short notes for each.
- Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
- Revise all the 2-mark questions the night before the exam.
- Finish with one full paper from the list above, timed.
Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.
Frequently asked questions
Are these AKTU Big Data questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.
How was this important questions list made?
We compared all 287 questions from 12 AKTU Big Data papers (2016-17 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.
Where can I download AKTU Big Data previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.
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