AKTU · B.Tech · Important questions
AKTU Data Warehousing and Data Mining Important Questions
The questions AKTU asks again and again in Data Warehousing and Data Mining, found by comparing every question in 8 previous year papers from 2016-17 to 2025-26 (subject codes BOE095, KAI075, KOE093, NCS066, RIT062).
How this list is made: we compared all 190 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.
- Explain web content mining and web structure mining.
- Explain web mining and types of web mining with example.
- What is web mining? Differentiate between web content mining, web structure mining and web usage mining.
- Describe web mining with differentiate between web content mining, web 10 structure mining and web suing mining.
- Enumerate the steps involved in mapping the data warehouse to a multiprocessor architecture.
- Discuss about the concept of Mapping the Data Warehouse to a Multiprocessor Architecture.
- Explain ROLAP, MOLAP and HOLAP in detail.
- Differentiate ROLAP, MOLAP and HOLAP Server Architectures
- Differentiate between ROLAP , HOLAP, and MOLAP . ROLAP, HOLAP MOLAP
- Explain the concept of building a Data Warehouse.
- Discuss in detail about the Building a Data Warehouse.
- Explain the ETL (Extract, Transform, and Load ) process and its significance in building a Data Warehouse. ( , )
- Compare OLTP and OLAP Systems.
- Write down the difference between OLTP and OLAP.
- Compare and contrast between Distance-Based Algorithms and Decision Tree-Based Algorithms.
- Compare and contrast between Distance-Bsed Algorithms and Decision Tree- 10 Based Algorithms.
- Describe data cubes with suitable example.
- Write down the difference between Database System and Data Cubes.
- Discuss CURE and Chameleon in detail?
- How CURE is different from CHAMELEON.
- Discuss about the Statistical measures in large Databases in Classification.
- Discuss the essential differences between the MOLAP &ROPLAP?
- What are the essential differences between the ROLAP and MOLAP?
- Explain DBSCAN Clustering algorithm
- Discuss DBSCAN clustering algorithm with suitable example.
- Explain Decision tree with a suitable example
- Discuss Decision tree-based classifiers in detail.
- Explain Market Basket Analysis.
- Explain Multi-Dimensional Data Model?
- Describe about the Difference between Database System and Data Warehouse. What do you mean by Multi-Dimensional Data Model?
- Explain Snow Flakes in detail.
- Write short notes on: i. Data Characterization and Data Discrimination ii.Snow Flakes in detail.
- Write short notes on: i. Steps of Knowledge Discovery in data ii. Explain Snow Flakes in detail.
- Explain Top- down and Bottom-up approach for Hierarchical Clustering
- Discuss the design and implementation of a Data Warehouse . Explain the "Top - down" vs. "Bottom - up" approach . " - "
- Explain Warehouse Schema Design.
- Discuss about the Warehousing Software and Warehouse Schema Design in Data Warehouse Process Technology.
- Explain various measures of central tendency.
- Describe briefly the measure of central tendency.
- How does a Snowflake schema differ from a STAR schema?
- “A data warehouse can be modeled by either a star schema or a snowflake schema”. With relevant examples discuss the two types of schema.
- Illustrate the OLAP function, OLAP tools and servers.
- Explain about the OLAP function, OLAP Tools and OLAP Servers.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Define Data Warehousing.
- Explain Data Warehousing?
- What do you mean by Data Warehousing?
- Define Data warehouse.
- Explain Data Warehouse Process.
- What do you mean by Data Warehouse Process?
- Explain the needs on Data Mining.
- Define Data Mining?
- What are the needs of Data Mining?
- What is classification?
- Explain about the Classification.
- Discuss about the Classification.
- Define Warehousing Software.
- Discuss Data Visualization.
- Write a short note on Data Visualization.
- Discuss the Clustering.
- Discuss about the Clustering.
- Discuss the Fact Constellation.
- Discuss the concept of Data Cleaning.
- Why data cleaning routines are needed?
- Explain Distributed DBMS implementation.
- Explain Web Mining?
- List any four applications of web mining .
- Explain data generalization.
- Describe data Generalization.
- Explain support and confidence in Association rule mining
- Illustrate the Warehousing Strategy.
- What do you understand by Warehousing Strategy?
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BOE095, 2024-25 KOE093, 2023-24 KOE093), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Describe the various OLAP Operations (Roll - up, Drill - down, Slice, Dice, and Pivot) with examples. (2025-26, BOE095)
- Explain the 3 - tier Architecture of a Data Warehouse with a neat diagram. (2025-26, BOE095)
- Explain Laplacian correction to avoid computing probability values of zero in Naïve Bayesian Classifier with example (2024-25, KOE093)
- Explain Top- down and Bottom-up approach for Hierarchical Clustering (2024-25, KOE093)
- How mapping a 2D table into multidimensional data model? Explain with suitable example. (2023-24, KOE093)
- Write short notes on: i. Data Characterization and Data Discrimination ii.Snow Flakes in detail. (2023-24, KOE093)
Unit 2
- Discuss the design and implementation of a Data Warehouse . Explain the "Top - down" vs. "Bottom - up" approach . " - " (2025-26, BOE095)
- Explain the ETL (Extract, Transform, and Load ) process and its significance in building a Data Warehouse. ( , ) (2025-26, BOE095)
- Explain K-mean Clustering Algorithm with it’s Limitations (2024-25, KOE093)
- Explain DBSCAN Clustering algorithm (2024-25, KOE093)
- Differentiate between: (i) Min-Max and Z-score Normalization with examples (ii) Binary data variables and Nominal data variables with examples (2023-24, KOE093)
- Explain the major components of Data Mining Architecture. (2023-24, KOE093)
Unit 3
- What do you mean by missing data values? Explain three Central Tendency Imputation Methods to fill the massing values with suitable examples . (2025-26, BOE095)
- What do you mean by normalization? Explain the Min - M ax, . Z - score and Decimal S caling normalization with suitable examples . (2025-26, BOE095)
- Explain The following iii. Spatial Mining iv. Temporal Mining (2024-25, KOE093)
- Explain the following i. Regression ii. Normalization (2024-25, KOE093)
- Discuss Decision tree-based classifiers in detail. (2023-24, KOE093)
- Classify the tuple X = (age = youth, income = medium, student= yes, 10 3 credit rating = fair) using Bayes Theorem. Class: RID age income student credit_rating buys_computer 1 youth high no fair no 2 youth high no excellent no 3 middle_aged high no fair yes 4 senior medium no fair yes 5 senior low yes fair yes 6 senior low yes excellent no (2023-24, KOE093)
Unit 4
- Explain the K - Means Clustering algorithm. What are its strengths and weaknesses? (2025-26, BOE095)
- How can you identify the Symmetric, Positively .skewed and negatively skewed data from the 5 - number summary box plot? Explain in detail with all statistical description used in Box plot . 5 - , (2025-26, BOE095)
- Explain the Following i. various measures to calculate the central tendency of Data ii. Standard deviation and Variance. (2024-25, KOE093)
- Explain Decision tree with a suitable example (2024-25, KOE093)
- Explain various types of clustering methods. Discuss any one partitioning clustering algorithm. (2023-24, KOE093)
- Discuss DBSCAN clustering algorithm with suitable example. (2023-24, KOE093)
Unit 5
- Differentiate between ROLAP , HOLAP, and MOLAP . ROLAP, HOLAP MOLAP (2025-26, BOE095)
- What do you mean by Time - Stamped Data in t emporal Mining ? Explain the different applications of temporal mining. (2025-26, BOE095)
- Explain web content mining and web structure mining. (2024-25, KOE093)
- Differentiate ROLAP, MOLAP and HOLAP Server Architectures (2024-25, KOE093)
- Differentiate between (a) OLAP and OLTP in detail. (b) Slice and Dice operations with an example. (2023-24, KOE093)
- Define Spatial Data? How mining of spatial data is done? (2023-24, KOE093)
Papers used for this list
- AKTU Data Warehousing and Data Mining (BOE095) question paper 2025-26
- AKTU Data Warehousing and Data Mining (KOE093) question paper 2024-25
- AKTU Data Warehousing and Data Mining (KAI075) question paper 2023-24
- AKTU Data Warehousing and Data Mining (KOE093) question paper 2023-24
- AKTU Data Warehousing and Data Mining (KOE093) question paper 2022-23
- AKTU Data Warehousing and Data Mining (KOE093) question paper 2021-22
- AKTU Data Warehousing and Data Mining (RIT062) question paper 2018-19
- AKTU Data Warehousing and Data Mining (NCS066) 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 Data Warehousing and Data Mining 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 190 questions from 8 AKTU Data Warehousing and Data Mining 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 Data Warehousing and Data Mining 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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