AL-603-B Data and Visual Analytics - Jun 2026 Question Paper

Under each question, How to answer lists the points an answer should cover, outlined by CampusPrep from the syllabus. Check the details against your textbook.

1. 7 Marks
   aExplain Data Indexing. Discuss different indexing techniques and their importance in efficient data retrieval.
   How to answer

   • Define data indexing and its purpose
   • Discuss indexing techniques e.g. ordered, hash, bitmap, tree-based
   • Explain importance for query speed and efficient retrieval
   • Give examples of use in databases/analytics
2. 7 Marks
   bExplain the importance of Measures of Dispersion in statistical analysis and decision making.
   How to answer

   • Define measures of dispersion e.g. range, variance, SD
   • Explain role in describing data spread and variability
   • Discuss use in statistical analysis and decision making
   • Give examples of risk and quality decisions
3. 7 Marks
   aExplain the concept of Sampling Distribution. Discuss its characteristics, importance and applications in statistical analysis.
   How to answer

   • Define sampling distribution
   • Describe characteristics e.g. mean, standard error
   • Explain importance in inference and hypothesis testing
   • List applications in statistical analysis
4. 7 Marks
   bExplain different methods of Data Categorization.
   How to answer

   • Define data categorization
   • Explain methods e.g. nominal, ordinal, grouping, clustering
   • Discuss criteria for categorization
   • Give examples
5. 7 Marks
   aDiscuss the role of Maximum Likelihood Estimation in Machine Learning and Data Analytics.
   How to answer

   • Define maximum likelihood estimation (MLE)
   • Explain principle of maximizing likelihood function
   • Discuss role in parameter estimation for ML models
   • Give examples in data analytics
6. 7 Marks
   bExplain Bayesian Networks. Discuss their structure and working.
   How to answer

   • Define Bayesian networks and probabilistic graphical models
   • Describe structure: nodes, edges, CPTs and DAG
   • Explain working with Bayes theorem and inference
   • Give applications
7. 7 Marks
   aWrite the Short Note:

   1. i)Principal Component Analysis (PCA)
   2. ii)Statistical Hypothesis Testing
   How to answer

   • Define PCA and dimensionality reduction
   • Define hypothesis testing and steps
   • Explain purpose and applications
   • Summarize key terms
8. 7 Marks
   bExplain the concept of Data Wrangling. Discuss its importance in Data Analytics.
   How to answer

   • Define data wrangling / munging
   • Describe steps: cleaning, transforming, enriching
   • Explain importance for analysis-ready data
   • Give tools and examples
9. 7 Marks
   aDescribe different sources of data used in Data Analytics.
   How to answer

   • List sources: databases, files, APIs, sensors, web, social media
   • Describe structured vs unstructured sources
   • Explain relevance to analytics
   • Give examples
10. 7 Marks
    bDefine Data Cleaning. Explain various types of data quality problems and discuss suitable methods to resolve them.
    How to answer

    • Define data cleaning
    • List data quality problems: missing, duplicate, inconsistent, outlier
    • Discuss methods to resolve each
    • Explain impact on analysis quality
11. 7 Marks
    aExplain the process of designing an effective dashboard. Discuss various components and design principles used in dashboard development.
    How to answer

    • Outline dashboard design process: requirements to deployment
    • Describe components: KPIs, charts, filters
    • Explain design principles: clarity, consistency, interactivity
    • Give best practices
12. 7 Marks
    bDefine Multivariate Data Exploration. Explain its objectives and significance in complex data analysis.
    How to answer

    • Define multivariate data exploration
    • List objectives: patterns, correlations, outliers
    • Explain significance in complex data analysis
    • Mention techniques and visualizations
13. 7 Marks
    aCompare CSV, JSON, XML, and Parquet file formats with suitable examples.
    How to answer

    • Describe CSV, JSON, XML, Parquet structures with examples
    • Compare on readability, schema, storage, use-case
    • Tabulate differences
    • Recommend when to use each
14. 7 Marks
    bExplain the architecture and working of Hadoop Distributed File System (HDFS). Discuss its advantages and limitations.
    How to answer

    • Describe HDFS architecture: NameNode, DataNode, blocks
    • Explain read/write working and replication
    • Discuss advantages: scalability, fault tolerance
    • Discuss limitations: latency, small files
15. 7 Marks
    aAnalyze the role of Plotly in Business Intelligence applications. Discuss why interactive dashboards are preferred over traditional charts.
    How to answer

    • Define Plotly and its interactive capabilities
    • Explain role in BI applications and dashboards
    • Compare interactive dashboards vs traditional charts
    • Give benefits with examples
16. 7 Marks
    bExplain how Power BI supports real-time analytics and interactive reporting.
    How to answer

    • Describe Power BI features for real-time data
    • Explain streaming, dashboards, alerts
    • Discuss interactive reporting: slicers, drill-through
    • Give BI use-cases