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AKTU · MCA · 3rd semester

AKTU MCA Data Warehousing & Data Mining (BMC012) Question Paper 2025-26

UniversityDr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow
CourseMCA, Semester III
SubjectData Warehousing & Data Mining (BMC012)
ExamTheory Examination 2025-26 (odd semester)
Time · Marks3 hours · 70 marks
SourceAKTU question paper, from the ABESIT question paper bank
View original PDF Other years

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

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

  1. State the primary objective of Data Warehousing.
  2. List any two components of a Data Warehouse architecture.
  3. State the role of ETL in Data Warehouse processes.
  4. Explain Data Cleaning.
  5. Define Decision Tree.
  6. Distinguish between Classification and Clustering.
  7. Explain Data Visualization.

Section B

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

  1. Discuss the process of building a Data Warehouse and explain the role of warehouse databases in decision support systems.
  2. Explore the fact constellations and snowflake schema in data warehousing.
  3. Explain data cleaning techniques for handling missing and noisy data.
  4. Explain in detail density-based clustering methods.
  5. Explain warehousing applications and recent trends.

Section C

Q3

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

  1. Describe the multidimensional data model and illustrate Star and Snowflake schemas with examples.
  2. Analyze the benefits and challenges of implementing a multidimensional data model in a data warehouse.

Q4

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

  1. Explain the data warehouse planning and implementation process, highlighting major management and support activities.
  2. Describe the importance of hardware and operating systems in data warehousing environments.

Q5

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

  1. Discuss the stages of data preprocessing and explain the role of data reduction techniques in data mining.
  2. Demonstrate the architecture of a data mining system.

Q6

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

  1. Explain classification techniques and compare statistical-based and distance- based algorithms.
  2. Explain Decision Tree construction and its application in knowledge discovery.

Q7

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

  1. Explain Web Mining, Spatial Mining and Temporal Mining.
  2. Explain OLAP functions, OLAP tools, and OLAP servers.

Short on time? See the most repeated AKTU Data Warehousing and Data Mining questions across all years.

Other years

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