Engineering Student Sample Exam Paper 2026 (RGPV)

CS703(B) – Data Mining and Warehousing

VII Semester – B.Tech Computer Science Engineering

Model Question Paper – Set A

Time: Three Hours
समय: तीन घंटे

Maximum Marks: 70
अधिकतम अंक: 70

Instructions / निर्देश

Attempt any five questions.
किन्हीं पाँच प्रश्नों को हल कीजिए।
All questions carry equal marks.
सभी प्रश्न समान अंक के हैं।
In case of any doubt, English version will be treated as final.
किसी भी शंका की स्थिति में अंग्रेज़ी संस्करण मान्य होगा।

Q.1

14 Marks / 14 अंक

(a) Explain Data Warehouse architecture and discuss different components of Data Warehouse.

Data Warehouse architecture को समझाइए तथा Data Warehouse के विभिन्न components की व्याख्या कीजिए।

(7 Marks)

(b) Explain Data Cleaning and Data Transformation techniques used in data preprocessing.

Data preprocessing में उपयोग की जाने वाली Data Cleaning एवं Data Transformation techniques को समझाइए।

(7 Marks)

Q.2

14 Marks / 14 अंक

(a) Explain OLAP operations with suitable examples (Roll-up, Drill-down, Slice and Dice).

उपयुक्त उदाहरण सहित OLAP operations (Roll-up, Drill-down, Slice एवं Dice) को समझाइए।

(7 Marks)

(b) Differentiate between OLTP and OLAP with suitable examples.

उपयुक्त उदाहरण सहित OLTP एवं OLAP में अंतर स्पष्ट कीजिए।

(7 Marks)

Q.3

14 Marks / 14 अंक

(a) Explain Data Mining and compare Data Mining with Knowledge Discovery in Database (KDD).

Data Mining को समझाइए तथा Data Mining एवं Knowledge Discovery in Database (KDD) की तुलना कीजिए।

(7 Marks)

(b) Explain Data Mining task primitives and issues in Data Mining.

Data Mining task primitives एवं Data Mining की समस्याओं (issues) को समझाइए।

(7 Marks)

Q.4

14 Marks / 14 अंक

(a) Explain Decision Tree classification algorithm with suitable example.

उपयुक्त उदाहरण सहित Decision Tree classification algorithm को समझाइए।

(7 Marks)

(b) Explain Statistical based and Rule based algorithms used in classification.

Classification में उपयोग होने वाले Statistical based एवं Rule based algorithms को समझाइए।

(7 Marks)

Q.5

14 Marks / 14 अंक

(a) Explain Hierarchical clustering and Partition clustering methods.

Hierarchical clustering तथा Partition clustering methods को समझाइए।

(7 Marks)

(b) Explain DBSCAN and BIRCH clustering algorithms with suitable examples.

उपयुक्त उदाहरण सहित DBSCAN एवं BIRCH clustering algorithms को समझाइए।

(7 Marks)

Q.6

14 Marks / 14 अंक

(a) Explain Apriori Algorithm with suitable example.

उपयुक्त उदाहरण सहित Apriori Algorithm को समझाइए।

(7 Marks)

(b) Explain FP Growth Algorithm and compare it with Apriori Algorithm.

FP Growth Algorithm को समझाइए तथा इसकी तुलना Apriori Algorithm से कीजिए।

(7 Marks)

Q.7

14 Marks / 14 अंक

(a) Explain Metadata and Data Mart used in Data Warehousing.

Data Warehousing में उपयोग होने वाले Metadata एवं Data Mart को समझाइए।

(7 Marks)

(b) Explain similarity measures and Data Quality in Data Mining.

Data Mining में similarity measures एवं Data Quality को समझाइए।

(7 Marks)

Q.8

14 Marks / 14 अंक

Attempt any two / किन्हीं दो को हल कीजिए

(a) Snowflake Schema

स्नोफ्लेक स्कीमा

(7 Marks)

(b) Fuzzy Sets and Fuzzy Logic

फज़ी सेट एवं फज़ी लॉजिक

(7 Marks)

CURE क्लस्टरिंग एल्गोरिथ्म

(7 Marks)

CS703(B) – Data Mining and Warehousing (VII Semester) | Model Question Paper – Set A | RGPV Style

Engineering Student Sample Exam Paper 2026 (RGPV)

CS703(B) – Data Mining and Warehousing

VII Semester – B.Tech Computer Science Engineering

Model Question Paper – Set B

Time: Three Hours
समय: तीन घंटे

Maximum Marks: 70
अधिकतम अंक: 70

Instructions / निर्देश

Attempt any five questions.
किन्हीं पाँच प्रश्नों को हल कीजिए।
All questions carry equal marks.
सभी प्रश्न समान अंक के हैं।
In case of doubt, English version will be treated as final.
किसी शंका की स्थिति में अंग्रेज़ी संस्करण मान्य होगा।

Q.1

14 Marks / 14 अंक

(a) Explain Data Warehouse implementation and discuss Data Warehouse building blocks.

Data Warehouse implementation को समझाइए तथा Data Warehouse building blocks की व्याख्या कीजिए।

(7 Marks)

(b) Explain Star Schema and Fact Constellation Schema with suitable example.

उपयुक्त उदाहरण सहित Star Schema एवं Fact Constellation Schema को समझाइए।

(7 Marks)

Q.2

14 Marks / 14 अंक

(a) Explain MOLAP and ROLAP with suitable comparison.

उपयुक्त तुलना सहित MOLAP एवं ROLAP को समझाइए।

(7 Marks)

(b) Explain categorization of OLAP tools and OLAP servers.

OLAP tools के वर्गीकरण तथा OLAP servers को समझाइए।

(7 Marks)

Q.3

14 Marks / 14 अंक

(a) Explain Data Mining functionalities and basic data mining tasks.

Data Mining functionalities एवं basic data mining tasks को समझाइए।

(7 Marks)

(b) Explain supervised and unsupervised learning with suitable examples.

उपयुक्त उदाहरण सहित supervised एवं unsupervised learning को समझाइए।

(7 Marks)

Q.4

14 Marks / 14 अंक

(a) Explain distance-based and probabilistic classifiers.

Distance-based एवं probabilistic classifiers को समझाइए।

(7 Marks)

(b) Differentiate between classification and clustering.

Classification एवं clustering में अंतर स्पष्ट कीजिए।

(7 Marks)

Q.5

14 Marks / 14 अंक

(a) Explain BIRCH and CURE clustering algorithms.

BIRCH एवं CURE clustering algorithms को समझाइए।

(7 Marks)

(b) Explain different types of data in cluster analysis.

Cluster analysis में प्रयुक्त विभिन्न प्रकार के data को समझाइए।

(7 Marks)

Q.6

14 Marks / 14 अंक

(a) Explain frequent item set generation using Apriori algorithm.

Apriori algorithm द्वारा frequent item set generation को समझाइए।

(7 Marks)

(b) Explain Association Rule Mining with suitable example.

उपयुक्त उदाहरण सहित Association Rule Mining को समझाइए।

(7 Marks)

Q.7

14 Marks / 14 अंक

(a) Explain Metadata and Partitioning Strategy in Data Warehouse.

Data Warehouse में Metadata एवं Partitioning Strategy को समझाइए।

(7 Marks)

(b) Explain handling missing values and data preprocessing techniques.

Missing values handling एवं data preprocessing techniques को समझाइए।

(7 Marks)

Q.8

14 Marks / 14 अंक

Attempt any two / किन्हीं दो को हल कीजिए

(a) Snowflake Schema

स्नोफ्लेक स्कीमा

(7 Marks)

(b) Data Quality

डेटा क्वालिटी

(7 Marks)

FP Growth एल्गोरिथ्म

(7 Marks)

CS703(B) – Data Mining and Warehousing (VII Semester) | Model Question Paper – Set B | RGPV Style

Engineering Student Sample Exam Paper 2026 (RGPV)

CS703(B) – Data Mining and Warehousing

VII Semester – B.Tech Computer Science Engineering

Model Question Paper – Set C

Time: Three Hours
समय: तीन घंटे

Maximum Marks: 70
अधिकतम अंक: 70

Instructions / निर्देश

Attempt any five questions.
किन्हीं पाँच प्रश्नों को हल कीजिए।
All questions carry equal marks.
सभी प्रश्न समान अंक के हैं।
In case of any doubt, English version will be treated as final.
किसी भी शंका की स्थिति में अंग्रेज़ी संस्करण मान्य होगा।

Q.1

14 Marks / 14 अंक

(a) Explain three-tier architecture of Data Warehouse with suitable diagram.

उपयुक्त आरेख सहित Data Warehouse की three-tier architecture को समझाइए।

(7 Marks)

(b) Explain Data Reduction and Data Integration techniques.

Data Reduction एवं Data Integration techniques को समझाइए।

(7 Marks)

Q.2

14 Marks / 14 अंक

(a) Explain OLAP queries and multidimensional data model with suitable example.

उपयुक्त उदाहरण सहित OLAP queries एवं multidimensional data model को समझाइए।

(7 Marks)

(b) Differentiate between ROLAP and MOLAP.

ROLAP एवं MOLAP में अंतर स्पष्ट कीजिए।

(7 Marks)

Q.3

14 Marks / 14 अंक

(a) Explain Data Mining functionalities and issues in Data Mining.

Data Mining functionalities तथा Data Mining की समस्याओं को समझाइए।

(7 Marks)

(b) Explain Data Quality and Similarity Measures used in Data Mining.

Data Mining में उपयोग होने वाली Data Quality एवं Similarity Measures को समझाइए।

(7 Marks)

Q.4

14 Marks / 14 अंक

(a) Explain Neural Network based and Rule based classification algorithms.

Neural Network based एवं Rule based classification algorithms को समझाइए।

(7 Marks)

(b) Explain Decision Tree induction algorithm with suitable example.

उपयुक्त उदाहरण सहित Decision Tree induction algorithm को समझाइए।

(7 Marks)

Q.5

14 Marks / 14 अंक

(a) Explain Hierarchical clustering and DBSCAN clustering algorithm.

Hierarchical clustering एवं DBSCAN clustering algorithm को समझाइए।

(7 Marks)

(b) Explain BIRCH clustering algorithm and compare it with CURE clustering.

BIRCH clustering algorithm को समझाइए तथा इसकी तुलना CURE clustering से कीजिए।

(7 Marks)

Q.6

14 Marks / 14 अंक

(a) Explain Association Rule Mining and Frequent Pattern Mining.

Association Rule Mining एवं Frequent Pattern Mining को समझाइए।

(7 Marks)

(b) Explain Apriori algorithm and FP Growth algorithm with comparison.

Apriori algorithm एवं FP Growth algorithm को समझाइए तथा उनकी तुलना कीजिए।

(7 Marks)

Q.7

14 Marks / 14 अंक

(a) Explain Metadata and Pattern Warehousing.

Metadata एवं Pattern Warehousing को समझाइए।

(7 Marks)

(b) Explain Fuzzy Sets and Fuzzy Logic with suitable example.

उपयुक्त उदाहरण सहित Fuzzy Sets एवं Fuzzy Logic को समझाइए।

(7 Marks)

Q.8

14 Marks / 14 अंक

Attempt any two / किन्हीं दो को हल कीजिए

(a) Data Mart

डेटा मार्ट

(7 Marks)

(b) Classification

क्लासिफिकेशन

(7 Marks)

OLAP ऑपरेशन्स

(7 Marks)

CS703(B) – Data Mining and Warehousing (VII Semester) | Model Question Paper – Set C | RGPV Style

Data Mining and Warehousing RGPV Notes in Hindi Question Papers - Computer Science Engineering Tutorials in Hindi

Download Data Mining and Warehousing RGPV Notes in Hindi previous year question papers for Computer Science Engineering Tutorials in Hindi 7th Semester Notes in Hindi. These RGPV question papers help you understand the exam pattern, important topics, and question distribution.

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Identify your weak areas and focus on them
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Data Mining and Warehousing RGPV Notes in Hindi

Set A

Engineering Student Sample Exam Paper 2026 (RGPV)

CS703(B) – Data Mining and Warehousing

Instructions / निर्देश

Q.1

Q.2

Q.3

Q.4

Q.5

Q.6

Q.7

Q.8

Set B

Engineering Student Sample Exam Paper 2026 (RGPV)

CS703(B) – Data Mining and Warehousing

Instructions / निर्देश

Q.1

Q.2

Q.3

Q.4

Q.5

Q.6

Q.7

Q.8

Set C

Engineering Student Sample Exam Paper 2026 (RGPV)

CS703(B) – Data Mining and Warehousing

Instructions / निर्देश

Q.1

Q.2

Q.3

Q.4

Q.5

Q.6

Q.7

Q.8

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Data Mining and Warehousing RGPV Notes in Hindi Question Papers - Computer Science Engineering Tutorials in Hindi

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