AL-802-C Big Data Analytics - May 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 the concept, characteristics (5Vs), and evolution of Big Data.
   How to answer

   • Definition and concept of Big Data as large, complex datasets
   • Explain 5Vs: Volume, Velocity, Variety, Veracity, Value
   • Evolution from traditional data to web, social media, IoT era
   • Need for Big Data analytics
2. 7 Marks
   bDiscuss challenges in Big Data and explain the infrastructure and technologies used for Big Data analytics.
   How to answer

   • Major challenges: storage, processing, velocity, variety, privacy, security
   • Infrastructure: distributed storage, clusters, cloud
   • Technologies: Hadoop, Spark, NoSQL, HDFS, YARN
   • Role of analytics tools and frameworks
3. 7 Marks
   aDescribe Hadoop architecture and explain the core components of the Hadoop ecosystem.
   How to answer

   • Hadoop architecture: master-slave, HDFS, YARN, MapReduce
   • Core ecosystem components: HDFS, YARN, MapReduce, Hive, Pig, HBase, Sqoop
   • Data flow and fault tolerance
   • Diagram of Hadoop ecosystem
4. 7 Marks
   bExplain HDFS and YARN architecture and their role in distributed data processing.
   How to answer

   • HDFS architecture: NameNode, DataNode, blocks, replication
   • YARN architecture: ResourceManager, NodeManager, ApplicationMaster
   • Role in distributed storage and resource management
   • Support for parallel data processing
5. 7 Marks
   aExamine the working of the MapReduce model and discuss its strengths and limitations in real-world applications.
   How to answer

   • MapReduce phases: Map, Shuffle, Sort, Reduce
   • Working with example e.g., word count
   • Strengths: scalability, fault tolerance, simplicity
   • Limitations: batch only, latency, no iterative support
6. 7 Marks
   bCompare Hadoop with traditional RDBMS systems in terms of data processing, scalability, and performance.
   How to answer

   • Basis: data type, schema, scalability, processing model
   • Hadoop for unstructured large-scale batch vs RDBMS for structured transactional
   • Performance and cost comparison
   • Use-case suitability
7. 7 Marks
   aAnalyze how Hive simplifies Big Data querying and discuss its limitations compared to traditional databases.
   How to answer

   • Hive architecture: HiveQL, metastore, execution on MapReduce/Tez
   • How it provides SQL-like querying on HDFS
   • Limitations: high latency, no OLTP, limited updates
   • Comparison with traditional databases
8. 7 Marks
   bEvaluate the execution model of Apache Pig and explain how it supports data transformation tasks.
   How to answer

   • Pig Latin dataflow model and execution steps
   • Compilation to MapReduce jobs
   • Support for ETL and data transformation tasks
   • Use cases for semi-structured data
9. 7 Marks
   aExplain NoSQL databases and discuss various NoSQL data models.
   How to answer

   • Definition and need for NoSQL: CAP theorem, scalability
   • Types: key-value, document, column-family, graph
   • Examples: MongoDB, Cassandra, HBase, Neo4j
   • Schema flexibility and use cases
10. 7 Marks
    bDescribe MongoDB architecture and its role in Big Data applications.
    How to answer

    • MongoDB document model, collections, sharding, replica sets
    • Architecture: mongod, mongos, config servers
    • Role in handling unstructured Big Data
    • Scalability and real-time applications
11. 7 Marks
    aDiscuss how graph theory is used to model social networks and extract meaningful insights.
    How to answer

    • Graph representation: nodes, edges for users and relations
    • Metrics: centrality, clustering, path analysis
    • Community and influence extraction
    • Examples of insight discovery
12. 7 Marks
    bEvaluate the applications of social network mining in real-world scenarios such as recommendation and fraud detection.
    How to answer

    • Recommendation systems using link prediction and similarity
    • Fraud detection via anomaly and community patterns
    • Real-world examples: e-commerce, banking
    • Benefits and effectiveness
13. 7 Marks
    aExamine clustering techniques used in social network graphs and analyze their effectiveness in community detection.
    How to answer

    • Clustering methods: k-means, Louvain, Girvan-Newman
    • Community detection approach on graphs
    • Effectiveness measures: modularity, conductance
    • Challenges with large dynamic graphs
14. 7 Marks
    bDiscuss the challenges involved in analyzing large-scale social network data and suggest possible solutions.
    How to answer

    • Challenges: volume, velocity, sparsity, privacy, scalability
    • Computational and storage issues
    • Possible solutions: distributed frameworks, sampling, graph databases
    • Privacy-preserving techniques
15. 7 Marks
    aDesign a Big Data processing pipeline for a real-world application (e.g., e-commerce or social media analytics) and justify the choice of tools.
    How to answer

    • Pipeline stages: ingestion, storage, processing, analysis, visualization
    • Example for e-commerce/social media with tools
    • Tool choice: Kafka, HDFS, Spark, Hive, Tableau
    • Justification on scalability and latency
16. 7 Marks
    bWrite short notes on:

    1. i)ETL Processing
    2. ii)Big Data Analytics Tools
    3. iii)Data Types in Hive and Pig
    How to answer

    • ETL: Extract, Transform, Load steps and tools
    • Big Data analytics tools: Hadoop, Spark, Hive, Pig
    • Hive vs Pig data types with examples
    • Use cases for each