JNTUK B.Tech CSE 3-1 CSE (R23) DWDM Unit Wise 10 Marks Important Questions and Answers

  JNTUK B.Tech CSE 3-1 CSE (R23) DWDM Unit Wise 10 Marks Important Questions and Answers are now available for all 5 units. Here we provide the out line of the anwers to every questions. The students are requested to explore rest of the answer their own.

UNIT – I: Data Warehousing and Online Analytical Processing

1. Explain the basic concepts of data warehousing.
   Ans: Discuss the characteristics of a data warehouse, its architecture, and its role in decision support systems.

2. Describe data cube technology in detail.
   Ans: Explain the concept of multidimensional data model, data cubes, cube operations (roll-up, drill-down, slice, dice, pivot), and their applications.

3. Explain the various steps in the design and implementation of a data warehouse.
   Ans: Cover data extraction, cleaning, transformation, loading, indexing, and OLAP server implementation.

4. Discuss different types of data, similarity, and dissimilarity measures with suitable examples.
   Ans: Include attribute types, types of data sets, distance measures between data objects, and summary statistics.

5. What is OLAP? Describe OLAP operations and OLAP server architectures.
   Ans: Discuss operations like roll-up, drill-down, slice, dice, pivot, and explain MOLAP, ROLAP, and HOLAP architectures.

UNIT – II: Data Preprocessing

1. Explain in detail the various data preprocessing techniques.
   Ans: Cover cleaning, integration, transformation, reduction, and discretization with examples.

2. Describe data cleaning techniques with examples.
   Ans: Explain how missing values, noisy data, and inconsistencies are handled using smoothing, binning, and other methods.

3. Explain different data transformation and normalization techniques.
   Ans: Discuss min–max, z-score, decimal scaling, aggregation, and attribute construction with formulas.

4. Discuss various data reduction techniques.
   Ans: Explain dimensionality reduction (e.g., PCA), numerosity reduction (e.g., histograms, clustering), and data compression methods.

5. What is data discretization? Explain various methods of discretization.
   Ans: Discuss binning, histogram analysis, entropy-based methods, and decision tree methods with suitable illustrations.

UNIT – III: Data Mining – Classification and Prediction

1. Explain the classification process in data mining with a neat diagram.
   Ans: Cover data preparation, model building, evaluation, and application.

2. Describe the construction of decision trees using ID3 algorithm with an example.
   Ans: Include steps, attribute selection using information gain, tree building, and pruning.

3. Explain the Bayesian classification approach in detail.
   Ans: Discuss the Bayesian theorem, naïve Bayes classifier, advantages, and limitations.

4. Discuss different methods for model evaluation and performance measurement.
   Ans: Include confusion matrix, accuracy, precision, recall, F1-measure, cross-validation, and ROC curves.

5. Differentiate between classification and prediction. Explain prediction methods.
   Ans: Discuss regression-based prediction and other numeric prediction methods with examples.

UNIT – IV: Association Analysis

1. Explain the problem definition of association rule mining with suitable examples.
   Ans: Define frequent itemsets, association rules, support, and confidence.

2. Describe the Apriori algorithm for frequent itemset generation.
   Ans:  Include Apriori principle, candidate generation, pruning, and a step-by-step example.

3. Explain the rule generation process in Apriori algorithm.
   Discuss confidence-based pruning and rule evaluation with an example.

4. Explain the FP-Growth algorithm in detail with example.
   Ans: Discuss FP-tree construction, mining frequent patterns, and compare with Apriori.

5. Write short notes on compact representation of frequent itemsets.
   Ans: Discuss closed and maximal frequent itemsets and their importance in reducing redundancy.

UNIT – V: Cluster Analysis

1. Explain the basic concepts of cluster analysis and its applications.
   Ans: Define clustering, cluster types, and discuss its importance in data mining.

2. Describe the K-means clustering algorithm in detail.
   Ans: Explain its steps, distance calculations, convergence, and issues like initialization and outliers with example.

3. Explain the Agglomerative Hierarchical Clustering algorithm.
   Ans: Describe its working procedure, dendrogram representation, and distance measures between clusters.

4. Discuss the DBSCAN algorithm in detail.
   Ans: Explain core points, border points, noise, density reachability, and advantages over K-means.

5. Compare partitioning, hierarchical, and density-based clustering methods.
   Ans: Highlight differences in methodology, output, advantages, and limitations.

Tips for Answering 10-Mark Questions in Exams

• Structure your answers clearly: Introduction → Main Concepts → Examples/Diagrams → Conclusion.
• Use neat labeled diagrams for decision trees, OLAP cubes, FP-tree, clustering structure, etc.
• Support explanations with small examples (e.g., K-means clustering on a 2D dataset).
• Use bullet points where appropriate for clarity.
• Write relevant formulas and define all variables properly.
• For algorithms, write stepwise procedure and explain with a small example — this fetches full marks.