AKTU Big Data · Important Questions 2026Source: ryzenstudy.com PYQs

BCS061 Big Data Important Questions 2026 (AKTU)

Repeat analysis + most-asked questions, unit-wise

What we did: built from all 4 AKTU Big Data papers (2021-22–2024-25): 97 questions grouped into 58 distinct questions, 23 of which were asked in more than one paper.

Last Updated: November 12, 2026

of marks were repeat questions: 65%; of marks were repeat questions
questions asked in 2+ papers: 23; questions asked in 2+ papers
papers analysed (2021-22–2024-25): 4; papers analysed (2021-22–2024-25)
units, mapped to current syllabus: 5; units, mapped to current syllabus

How much of the paper repeats?

Repeats are real: when we checked each newer BCS061 paper against the papers before it, on average 65% of its marks were questions already asked in an earlier paper.
2023-24 paper: 47% of marks were repeats (8 of 11 repeated questions were in our top list).
2024-25 paper: 83% of marks were repeats (11 of 18 repeated questions were in our top list).
Topic weightage is a guide, not a guarantee: our Tier 1 + Tier 2 topics covered 66% of the next paper's marks — picking the same number of topics at random would have covered about 49%.

Across 10 subjects and 23 papers we tested, 43% of marks repeated earlier questions on average.

1.Unit-wise most-asked questions

Ranked by how many papers asked them, then by how recently. Click a paper to open it.

Unit 1: Introduction to Big Data

18% of past marks

Question	Asked
Detail about the analysis vs. reporting while introducing th e Big Data 7 marks2022-23 Q3(a)2023-24 Q2(a)2024-25 Q3(b)	Asked 3×
List any five big data platforms 2 marks2021-22 Q1(a)2022-23 Q1(a)2024-25 Q1(a)	Asked 3×
Discuss the 5 Vs used in Big Data with the help of an example 7 marks2023-24 Q3(a)2024-25 Q3(a)	Asked 2×
What are the different types of digital data commonly encountered in Big Data applications? Provide examples of structured, semi-structured, and unstructured data 2 marks2021-22 Q3(a)2023-24 Q1(a)	Asked 2×

Unit 2: Hadoop · Map Reduce

17% of past marks

Question	Asked
Explain the key components of the Hadoop ecosystem and describe how they work together to enable large-scale data storage, processing, and analysis 7 marks2022-23 Q2(a)2023-24 Q2(b)2024-25 Q1(e)2024-25 Q1(f)2024-25 Q4(b)	Asked 3×
Discuss the detailed architecture of Map-Reduce 7 marks2021-22 Q2(b)2021-22 Q4(a)2022-23 Q4(a)2024-25 Q2(c)	Asked 3×
How does Apache Hadoop help process the data? 2 marks2022-23 Q1(b)2024-25 Q1(b)	Asked 2×
Discuss the process of developing a MapReduce application. What are the key steps involved in writing, testing, and deploying a Map Reduce program? 10 marks2023-24 Q4(b)	Asked 1×

Unit 3: HDFS (Hadoop Distributed File System) · Hadoop Environment

28% of past marks

Question	Asked
Illustrate the process of reading a block in Hadoop 7 marks2021-22 Q2(c)2022-23 Q2(c)2023-24 Q5(a)2024-25 Q5(b)	Asked 4×
Demonstrate the design of HDFS and concept in detail 7 marks2021-22 Q5(a)2022-23 Q5(a)2023-24 Q4(a)2024-25 Q5(a)	Asked 4×
Discuss in brief about the cluster specification. Describe how to setting up a Hadoop Cluster? 7 marks2022-23 Q5(b)2024-25 Q2(d)	Asked 2×
Discuss the advantages and challenges of deploying Hadoop in a cloud environment. Explain how cloud platforms enhance the scalability, flexibility, and cost-efficiency of Hadoop clusters 7 marks2023-24 Q5(b)2024-25 Q6(b)	Asked 2×

Unit 4: NoSQL Databases

18% of past marks

Question	Asked
Explain the operations for creating, updating, and deleting documents in MongoDB. What are the MongoDB CRUD operations, and how are they used to manipulate data in collections? 10 marks2021-22 Q2(d)2022-23 Q2(d)2023-24 Q6(a)	Asked 3×
Define NoSQL databases. What are the key characteristics and benefits of NoSQL databases compared to traditional relational databases? 10 marks2021-22 Q1(g)2022-23 Q1(g)2023-24 Q2(d)	Asked 3×
Discuss Master Slave and Peer-Peer replication in detail 7 marks2022-23 Q2(b)2024-25 Q2(b)	Asked 2×
Explain the components of features and components of Apache Spark 7 marks2022-23 Q6(a)2024-25 Q6(a)	Asked 2×

Unit 5: Hadoop Eco System Frameworks

19% of past marks

Question	Asked
Explain the architecture of HIVE. Also explain data flow in HIVE 7 marks2021-22 Q7(b)2022-23 Q2(e)2022-23 Q7(a)2023-24 Q2(e)2024-25 Q2(e)	Asked 4×
Compare and Contrast (i) Apache Pig vs Map-Reduce (ii) Pig vs SQL (iii) Pig vs HIVE 7 marks2022-23 Q7(b)2024-25 Q7(b)	Asked 2×
Explain the role of Pig Latin as a high-level scripting language for processing large datasets in cloud environments. How does using Pig Latin on cloud-based Hadoop platforms simplify data transformation and analysis compared to traditional MapReduce programming? 7 marks2024-25 Q7(a)	Asked 1×
How does Hive differ from Pig in the Hadoop ecosystem? 2 marks2024-25 Q1(d)	Asked 1×

2.Topic weightage: which topics carry the marks

Blue = Tier 1 + Tier 2 (topics that carried ~80% of past marks). Grey = the rest. It shows where marks have gone, not a promise of what comes next.

HDFS design and concepts · U3

10.6% (cum 11%)

Hive architecture and services · U5

7.4% (cum 18%)

Hadoop and its ecosystem · U2

6.9% (cum 25%)

Introduction to NoSQL · U4

6.8% (cum 32%)

MapReduce framework and architecture · U2

5.8% (cum 38%)

HDFS read and write · U3

5.4% (cum 43%)

Pig vs MapReduce vs Hive · U5

5.1% (cum 48%)

Big Data platforms and architecture · U1

4.8% (cum 53%)

YARN and schedulers · U3

4.7% (cum 58%)

MongoDB CRUD · U4

4.1% (cum 62%)

Analysis vs reporting · U1

4% (cum 66%)

5 Vs and characteristics · U1

3.9% (cum 70%)

Hadoop cluster setup · U3

2.7% (cum 72%)

Hadoop in the cloud · U3

2.5% (cum 75%)

Spark basics · U4

2.5% (cum 77%)

Applications and importance · U1

1.9% (cum 79%)

HiveQL and tables · U5

1.9% (cum 81%)

Pig Latin and Grunt · U5

1.7% (cum 83%)

Blocks and replication · U3

1.7% (cum 84%)

Types of digital data · U1

1.7% (cum 86%)

Pig introduction and modes · U5

1.7% (cum 88%)

Big Data analytics · U1

1.5% (cum 89%)

MongoDB basics · U4

1.3% (cum 91%)

Developing a MapReduce application · U2

1.3% (cum 92%)

Resilient Distributed Datasets · U4

1.3% (cum 93%)

Scala · U4

1.3% (cum 95%)

HBase · U5

1.3% (cum 96%)

Analyzing data and scaling out · U2

1.2% (cum 97%)

MongoDB indexing · U4

1.2% (cum 98%)

Shuffle, sort and job execution · U2

0.7% (cum 99%)

Hadoop streaming and pipes · U2

0.5% (cum 99%)

MapReduce formats and data format · U2

0.5% (cum 100%)

Data ingest: Flume and Sqoop · U3

0.5% (cum 100%)

Topic	Papers	Share
HDFS design and concepts · Unit 3 · Tier 1	4/4	10.6%
Hive architecture and services · Unit 5 · Tier 1	4/4	7.4%
Hadoop and its ecosystem · Unit 2 · Tier 1	3/4	6.9%
Introduction to NoSQL · Unit 4 · Tier 1	4/4	6.8%
MapReduce framework and architecture · Unit 2 · Tier 1	3/4	5.8%
HDFS read and write · Unit 3 · Tier 1	4/4	5.4%
Pig vs MapReduce vs Hive · Unit 5 · Tier 1	3/4	5.1%
Big Data platforms and architecture · Unit 1 · Tier 1	4/4	4.8%
YARN and schedulers · Unit 3 · Tier 2	3/4	4.7%
MongoDB CRUD · Unit 4 · Tier 2	3/4	4.1%
Analysis vs reporting · Unit 1 · Tier 2	3/4	4%
5 Vs and characteristics · Unit 1 · Tier 2	3/4	3.9%
Hadoop cluster setup · Unit 3 · Tier 2	2/4	2.7%
Hadoop in the cloud · Unit 3 · Tier 2	2/4	2.5%
Spark basics · Unit 4 · Tier 2	2/4	2.5%
Applications and importance · Unit 1 · Tier 2	2/4	1.9%
HiveQL and tables · Unit 5 · Tier 2	2/4	1.9%
Pig Latin and Grunt · Unit 5 · Tier 3	2/4	1.7%
Blocks and replication · Unit 3 · Tier 3	3/4	1.7%
Types of digital data · Unit 1 · Tier 3	2/4	1.7%
Pig introduction and modes · Unit 5 · Tier 3	2/4	1.7%
Big Data analytics · Unit 1 · Tier 3	1/4	1.5%
MongoDB basics · Unit 4 · Tier 3	1/4	1.3%
Developing a MapReduce application · Unit 2 · Tier 3	1/4	1.3%
Resilient Distributed Datasets · Unit 4 · Tier 3	1/4	1.3%
Scala · Unit 4 · Tier 3	1/4	1.3%
HBase · Unit 5 · Tier 3	1/4	1.3%
Analyzing data and scaling out · Unit 2 · Tier 3	1/4	1.2%
MongoDB indexing · Unit 4 · Tier 3	1/4	1.2%
Shuffle, sort and job execution · Unit 2 · Tier 3	1/4	0.7%
Hadoop streaming and pipes · Unit 2 · Tier 3	1/4	0.5%
MapReduce formats and data format · Unit 2 · Tier 3	1/4	0.5%
Data ingest: Flume and Sqoop · Unit 3 · Tier 3	1/4	0.5%

3.Paper pattern (2024-25)

Section	Questions	Rule
A	7 × 2 marks	Attempt all
B	5 × 7 marks	Attempt any 3
C	10 × 7 marks	Attempt any 1

4.Study order

1HDFS design and concepts (Unit 3, Tier 1)
2Hive architecture and services (Unit 5, Tier 1)
3Hadoop and its ecosystem (Unit 2, Tier 1)
4Introduction to NoSQL (Unit 4, Tier 1)
5MapReduce framework and architecture (Unit 2, Tier 1)
6HDFS read and write (Unit 3, Tier 1)
7Pig vs MapReduce vs Hive (Unit 5, Tier 1)
8Big Data platforms and architecture (Unit 1, Tier 1)
9YARN and schedulers (Unit 3, Tier 2)
10MongoDB CRUD (Unit 4, Tier 2)
11Analysis vs reporting (Unit 1, Tier 2)
125 Vs and characteristics (Unit 1, Tier 2)
13Hadoop cluster setup (Unit 3, Tier 2)
14Hadoop in the cloud (Unit 3, Tier 2)
15Spark basics (Unit 4, Tier 2)
16Applications and importance (Unit 1, Tier 2)
17HiveQL and tables (Unit 5, Tier 2)

FAQ

Which units of BCS061 carry the most marks?

Unit 3 (HDFS (Hadoop Distributed File System) · Hadoop Environment, 28% of past marks) and Unit 5 (Hadoop Eco System Frameworks, 19% of past marks).

Do AKTU Big Data questions repeat?

Yes. On average 65% of the marks in each newer BCS061 paper came from questions already asked in an earlier paper, and 23 questions have appeared in more than one paper.

How is this list made?

Every question from the 4 papers (2021-22–2024-25) was mapped to the current AKTU syllabus and grouped with the same question from other years. Questions are ranked by how many papers they appeared in, then by how recently.

Study this subject

Note: this is based on past AKTU papers, not a guarantee. Questions are mapped to the current syllabus; topics that are no longer in the syllabus are left out.