AKTU · MCA · Important questions
AKTU MCA 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 5 previous year papers from 2021-22 to 2025-26 (subject codes BMC012, KCA012).
How this list is made: we compared all 122 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.
- Describe various OLAP operations with example.
- Describe various OLAP operations with an example.
- Describe different types of OLAP operations with examples.
- Describe various OLAP operations in multidimensional data model.
- Explain web mining in detail
- Write Notes on i) Web Mining 10 5 ii) Spatial Mining
- List various algorithms used in web mining. Briefly discuss any one of them.
- Define data warehouse. What are the key features of data warehouse?
- What do you mean by Data Warehouse? Discuss the key features with suitable examples.
- Describe in detail about the Hardware and Operating Systems for Data Warehousing.
- Describe the importance of hardware and operating systems in data warehousing environments.
- Describe the basic architecture of data mining.
- Demonstrate the architecture of a data mining system.
- Describe the steps involved in data mining when viewed as a process of knowledge discovery.
- Discuss in detail the security issues in Data Warehousing.
- What is data warehouse planning? Explain its activities.
- Explain the data warehouse planning and implementation process, highlighting major management and support activities.
- What is temporal mining? Explain in detail
- Explain Web Mining, Spatial Mining and Temporal Mining.
- Why data cleaning is required? Discuss the methods of data cleaning.
- What do you mean by cleaning of the data? Explain the important types of data cleaning.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Define Data cleaning.
- Explain Data Cleaning.
- Define web mining.
- Explain Data Visualization.
- What do you mean by data visualization?
- How data mining is different from data warehousing.
- What is SMP?
- What is data generalization?
- Write down the components of data warehouse.
- List any two components of a Data Warehouse architecture.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BMC012, 2024-25 KCA012, 2023-24 KCA012), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Describe the multidimensional data model and illustrate Star and Snowflake schemas with examples. (2025-26, BMC012)
- Analyze the benefits and challenges of implementing a multidimensional data model in a data warehouse. (2025-26, BMC012)
- Why data cleaning is required? Discuss the methods of data cleaning. (2024-25, KCA012)
- Describe in detail about the Hardware and Operating Systems for Data Warehousing. (2024-25, KCA012)
- Briefly discuss the three schemas for multidimensional databases that consists of 4 dimensions (Time, Branch, Item, Location). Include the 10 2 appropriate measures required for the schemas. (2023-24, KCA012)
- Discuss why do we need to pre-process the data. Mention the various tasks to be accomplished as part of pre-processing. Elaborate each. (2023-24, KCA012)
Unit 2
- Explain the data warehouse planning and implementation process, highlighting major management and support activities. (2025-26, BMC012)
- Describe the importance of hardware and operating systems in data warehousing environments. (2025-26, BMC012)
- Discuss the KDD process in detail. (2024-25, KCA012)
- Assume we have the following dataset of 6 samples, each with features, and a binary class label: Sample Age Income Class 1 25 High No 2 30 High No 3 35 Medium Yes 4 40 Medium Yes 5 50 Low Yes 6 60 Low No The entropy of the entire data set is 1, Entropy of the splitting attribute Age is 1, Weighted Entropy of the splitting attribute Age is 1, Entropy of the splitting attribute Income is 0, Weighted Entropy of the splitting attribute Income is .33. Draw the decision tree. (2024-25, KCA012)
- Draw the Data Warehouse architecture and discuss its various components. (2023-24, KCA012)
- Discuss the various applications of Data Warehousing? (2023-24, KCA012)
Unit 3
- Discuss the stages of data preprocessing and explain the role of data reduction techniques in data mining. (2025-26, BMC012)
- Demonstrate the architecture of a data mining system. (2025-26, BMC012)
- How distance-based technique is used for data mining? (2024-25, KCA012)
- Transaction List of Items T1 I1, I2, I3, I5 T2 I2, I3, I4 T3 I4, I5 T4 I2, I4, I5 T5 I1, I2, I3, I5 T6 I1, I2, I3, I4 Support = 50%, Confidence = 60% Generate association rule for given items using Apriori Algorithms. (2024-25, KCA012)
- How classification is different from clustering? Discuss Decision Tree based classification technique in detail. (2023-24, KCA012)
- What do you mean by Data Extraction? Discuss the tools used for Data Extraction. (2023-24, KCA012)
Unit 4
- Explain classification techniques and compare statistical-based and distance- based algorithms. (2025-26, BMC012)
- Explain Decision Tree construction and its application in knowledge discovery. (2025-26, BMC012)
- Discuss Hierarchical Clustering in detail. (2024-25, KCA012)
- Write notes on i) STING ii) CLIQUE (2024-25, KCA012)
- How Bayes Theorem can be used for classification task? Explain (2023-24, KCA012)
- Write short notes on i) CURE 10 4 ii) DNSCAN (2023-24, KCA012)
Unit 5
- Explain Web Mining, Spatial Mining and Temporal Mining. (2025-26, BMC012)
- Explain OLAP functions, OLAP tools, and OLAP servers. (2025-26, BMC012)
- Discuss in detail the security issues in Data Warehousing. (2024-25, KCA012)
- What is temporal mining? Explain in detail (2024-25, KCA012)
- Discuss in detail the security issues in Data Warehousing. (2023-24, KCA012)
- Write Notes on i) Web Mining 10 5 ii) Spatial Mining (2023-24, KCA012)
Papers used for this list
- AKTU Data Warehousing and Data Mining (BMC012) question paper 2025-26
- AKTU Data Warehousing and Data Mining (KCA012) question paper 2024-25
- AKTU Data Warehousing and Data Mining (KCA012) question paper 2023-24
- AKTU Data Warehousing and Data Mining (KCA012) question paper 2022-23
- AKTU Data Warehousing and Data Mining (KCA012) question paper 2021-22
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 122 questions from 5 AKTU Data Warehousing and Data Mining papers (2021-22 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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