AD-801 Big Data - Jun 2025 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 significance of Big Data in various domains and industries.
   How to answer

   • Define Big Data and its Vs
   • Significance in healthcare, finance, retail, manufacturing, government, etc.
   • Examples of use-cases in each domain
   • Benefits for decision-making and innovation
2. 7 Marks
   bExplain the concept of data veracity in the context of Big Data and discuss its implications for data analysis and decision-making.
   How to answer

   • Define data veracity as accuracy, trustworthiness of data
   • Explain veracity as one of Big Data Vs
   • Implications: data quality, noise, bias
   • Impact on analysis and decision-making with mitigation
3. 14 Marks
   Define HDFS. Describe namenode, datanode and block. Explain HDFS operations in detail.
   How to answer

   • Define HDFS and its design goals
   • Explain NameNode, DataNode and blocks with functions
   • Describe read/write/replication operations
   • Explain fault tolerance and HDFS commands flow
4. 7 Marks
   aDescribe the physical architecture of Hive, focusing on its components and their interactions within a Hadoop ecosystem.
   How to answer

   • Overview of Hive in Hadoop ecosystem
   • Components: HiveServer, Metastore, Driver, Compiler, Execution engine
   • Interaction with HDFS, MapReduce/YARN
   • Data flow diagram of query execution
5. 7 Marks
   bDefine RDBMS and Hadoop and explain their architectures.
   How to answer

   • Define RDBMS with characteristics
   • Define Hadoop with core components
   • Explain RDBMS architecture vs Hadoop master-slave architecture
   • Comparison of scalability and data handling
6. 14 Marks
   What is Mongo DB? Explain in brief key features of Mongo DB. Show basic CRUD operations in Mongo DB with proper example.
   How to answer

   • Define MongoDB as NoSQL document database
   • Key features: schema-less, scalability, indexing, aggregation
   • CRUD operations: insert, find, update, delete
   • Example commands for each CRUD operation
7. 14 Marks
   Explain the following term:

   1. i)Social Network as Graph
   2. ii)Hive query language
   3. iii)YARN
   How to answer

   • Define Social Network as Graph with nodes and edges
   • Define Hive Query Language and its SQL-like nature
   • Define YARN with ResourceManager and NodeManager
   • Use in Big Data processing
8. 7 Marks
   aWhat is social network mining, and how does it differ from traditional data mining?
   How to answer

   • Define social network mining
   • Define traditional data mining
   • Differences: graph structure, scale, dynamics
   • Examples of data types and techniques
9. 7 Marks
   bDescribe the role of social network mining in detecting and preventing online fraud and cyber-attacks.
   How to answer

   • Role in fraud detection: anomaly and pattern detection
   • Graph-based techniques: community detection, link analysis
   • Prevention of cyber-attacks with real-time monitoring
   • Examples of phishing, fake accounts detection
10. 7 Marks
    aExplain NoSQL and write differences between NoSQL and SQL.
    How to answer

    • Define NoSQL and its types
    • Define SQL/RDBMS characteristics
    • Differences in schema, scalability, query language
    • Use-cases comparison in tabular form
11. 7 Marks
    bDiscuss the types of recommendation approaches commonly used in recommender systems.
    How to answer

    • Define recommender systems
    • Types: collaborative, content-based, hybrid, knowledge-based
    • Working of each approach with example
    • Applications and limitations
12. 14 Marks
    Write a short note (any three):

    1. i)Hadoop distributed file
    2. ii)Challenges with big data
    3. iii)Properties of Big data systems
    4. iv)Data types of pig
    How to answer

    • Select any three topics for short notes
    • Define each with key features and components
    • Explain relevance to Big Data
    • Give examples where applicable