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EndsemMay 20242019 patternBE AI&DS

Business Intelligence SPPU Endsem Question Paper May 2024

All 8 questions from this SPPU endsem paper, as printed. Read them here or open the PDF.

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Subject code
417533 B
Pattern
2019 pattern
Exam
Endsem
Session
May 2024
Max. marks
70
Time
2½ Hours
Semester
VIII
Paper code
[6263]-397

Printed for: B.E. (Artificial Intelligence and Data Science)

Instructions to the candidates

  1. Answer Q.1 or Q.2, Q.3 or Q.4, Q. 5 or Q. 6, Q. 7 or Q. 8.
  2. Neat diagrams must be drawn wherever necessary
  3. Assume suitable data if necessary.
  4. Figures to the right indicate full marks.

Questions

  1. Q1

    1. a)

      Evaluate the impact of Data Duplication on the overall performance and efficiency of a data warehouse

      [8]
    2. b)

      Explain the concepts of ETL Architecture, Extraction, Transformation, and Loading (ETL) in the context of Business Intelligence.

      [5]
    3. c)

      Explain the concept of Change Data Capture and its significance in data provisioning.

      [5]

  2. Q2

    1. a)

      Analyse the role of Lookups in the transformation process of ETL.

      [8]
    2. b)

      Create a simplified data mart and explain its purpose in a BI system.

      [5]
    3. c)

      Describe the key components of Data Provisioning, including Data Quality, Data Profiling, and Data Enrichment.

      [5]

  3. Q3

    1. a)

      Evaluate the effectiveness of different methods of Data discretization in improving the interpretability of data.

      [8]
    2. b)

      Describe the significance of Data Reduction in the context of large datasets, and explain the methods of Sampling.

      [5]
    3. c)

      Identify the types of data affected by noise and explain their impact on data quality. 2

      [4]

  4. Q4

    1. a)

      Compare and contrast Feature Selection and Principal component Analysis (PCA) in terms of their applicability and limitations.

      [8]
    2. b)

      Differentiate between univariate, Bivariate and Multivariate analysis.

      [5]
    3. c)

      Discuss the importance of Data Exploration in the data analysis pipeline.

      [4]

  5. Q5

    1. a)

      This part has equations that don’t show as text. Open the PDF to read it.

      [9]
    2. b)

      Define Bayesian methods and Logistic Regression in the context of classification.

      [5]
    3. c)

      List and explain the key components involved in the evaluation of regression models.

      [4]

  6. Q6

    1. a)

      Explain logistic regression with example considering relevant variables and data.

      [9]
    2. b)

      Discuss the principles underlying the Apriori Algorithm for association rule mining.

      [5]
    3. c)

      Define Hierarchical methods in clustering and describe their characteristics. 3

      [4]

  7. Q7

    1. a)

      Evaluate the benefits and challenges of integrating Cloud Computing with Business Intelligence.

      [9]
    2. b)

      Explain the importance of Business Intelligence in enhancing Customer Relationship Management strategies.

      [5]
    3. c)

      Provide examples of Business Intelligence applications in the Manufacturing Industry.

      [3]

  8. Q8

    1. a)

      Create a plan for integrating Web 2.0 and Online Social Networking features into existing Business Intelligence systems.

      [9]
    2. b)

      Describe the impact of Business Intelligence on Healthcare Monitoring processes and outcomes.

      [5]
    3. c)

      List and briefly explain the applications of Business Intelligence in the Higher Education sector. 

      [3]