AKTU MCA Data A Nalytics (BMC034) Question Paper 2025-26

Note: Attempt all Sections. If require any missing data; then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. Name the five V’s of Big Data.
2. Mention any four applications of data analytics.
3. Differentiate between linear and nonlinear systems.
4. What is Real - Time Analytics Platform (RTAP) ? Mention two applications of RTAP.
5. Write any four applications of clustering techniques.
6. Write any four differences between SQL and NoSQL databases.
7. What is exploratory data analysis (EDA)?

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Explain the roles and responsibilities of data scientists, business analysts, and project managers in analytics projects.
2. Given two fuzzy sets:
   A= {(x , 0.2), (x , 0.5), (x , 0.7), (x , 1.0)}
B= {(x , 0.6), (x , 0.4), (x , 0.8), (x , 0.3)}
   Compute the following:
   (i) Union A ∪ B (ii) Intersection A∩B (iii) Complement of A
3. E xplain stream computing and compare it with traditional batch processing.
4. Compare agglomerative and divisive hierarchical clustering methods.
5. Discuss applications of R, and visualization tools in real - world analytics systems.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain how analytics improves business intelligence and strategic planning.
2. Explain the concept of database sharding and its importance in distributed systems.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Compare traditional data processing systems with Big Data platforms.
2. S tate any two advantages and disadvantages of using K - means clustering. A K - means clustering problem has centroids at (2, 3) and (6, 7). Determine the nearest centroid for point

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. Define Bayesian inference. A Bayesian classifier predicts probability P(A)=0.7 and P(B)=0.5. Find P(A∩B) assuming independence.
2. Discuss the role of Apache Kafka and Apache Spark in stream analytics.

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Define the role of covariance in data analytics. Calculate the covariance between datasets X=(1, 2, 3) and Y=(2, 4, 6).
2. Compare the CLIQUE and ProCLUS clustering algorithms with respect to their working principles, dimensionality reduction approach, clustering strategy, advantages, limitations, and applications.

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain future trends and emerging technologies in stream analytics and real - time computing.
2. Discuss the Hadoop framework in detail, explaining the working of the Hadoop Distributed File System (HDFS) along with the advantages and limitations of Hadoop in large - scale data processing and storage.

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