SECTION A – Conceptual Basics (Short Answers)
Section A tests the fundamental understanding of Data Warehousing and Data Mining concepts. Each question is short and direct, so answers should be precise, definition-focused, and to the point.
This section mainly covers:
The basic meaning and purpose of Data Mining
Core concepts like classification, association rules, and data generalization
The importance of data preprocessing before mining
Fundamental ideas such as data compression, backup, and recovery
Students are expected to:
Write clear definitions
Use simple examples where required
Avoid lengthy explanations
This section checks whether the student understands core terminology used throughout the subject.
SECTION B – Descriptive & Comparative Questions
Section B focuses on detailed explanations and comparisons. Questions here require concept clarity, structured answers, and logical flow.
Major areas tested in this section include:
Data cleaning and its different phases
Clustering requirements in data mining
Comparison between STAR schema and Snowflake schema
Concepts like aggregation, historical data, and query facilities
Data warehousing along with its advantages and disadvantages
To score well in this section:
Answers should be well-explained, not just definitions
Use headings or step-wise explanation
Comparisons should be written in a clear contrast format
Real-world or practical relevance should be highlighted wherever possible
This section evaluates the student’s depth of understanding and ability to explain concepts properly.
SECTION C – Analytical & Application-Based Questions
Section C is designed to test analytical thinking and application of concepts. Questions are more focused and require logical explanation or numerical analysis.
This section includes:
ETL process: data integration, transformation, and loading
Data reduction techniques and strategies
Numerical problems (mean, variance, standard deviation)
Comparative study of clustering algorithms like CURE vs CHAMELEON
Density-based and Grid-based clustering
Differences between OLTP and OLAP
Data warehouse architectures
Web mining and its types
Comparison of ROLAP and MOLAP
To answer effectively:
Numerical problems must show proper steps
Theory answers should include examples or diagrams (if possible)
Comparisons should clearly explain why one approach differs from another
This section checks whether students can apply theoretical knowledge to solve problems and explain advanced concepts.
