CS-702-D Big Data - Nov 2022 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
   aList out the characteristics of Big data and Challenges in handling big data.
   How to answer

   • Define Big Data and list characteristics / Vs (Volume, Velocity, Variety, Veracity, Value).
   • List challenges: storage, processing, data quality, privacy, scalability, skilled resources.
   • Brief explanation of each challenge in handling big data.
   • Concluding remark on need for big data technologies.
2. 7 Marks
   bExplain various types of Big Data analytics and explain about Predictive analytics.
   How to answer

   • Define Big Data analytics and its types (descriptive, diagnostic, predictive, prescriptive).
   • Explain each type briefly with example.
   • Explain predictive analytics in detail: techniques, workflow and uses.
   • Mention applications of predictive analytics.
3. 7 Marks
   aDiscuss various technologies available for Big Data.
   How to answer

   • List technologies available for Big Data (Hadoop, Spark, NoSQL, HDFS, MapReduce, etc.).
   • Brief function of each technology.
   • How they support storage and processing of large data.
4. 7 Marks
   bWhy Hadoop is called a Big data technology? Explain how it supports Big data?
   How to answer

   • Define Hadoop and its relation to Big Data Vs.
   • Explain Hadoop features: distributed storage, fault tolerance, scalability.
   • Explain HDFS and MapReduce/YARN support for Big Data.
   • Conclude why it is called Big Data technology.
5. 7 Marks
   aBriefly discuss about MapReduce and YARN.
   How to answer

   • Define MapReduce: Map and Reduce phases with workflow.
   • Define YARN: components ResourceManager, NodeManager, ApplicationMaster.
   • Role of YARN in resource management and job scheduling.
   • Relation between MapReduce and YARN.
6. 7 Marks
   bExplain in detail about HDFS.
   How to answer

   • Define HDFS and its need for Big Data storage.
   • Explain architecture: NameNode, DataNode, blocks, replication.
   • Explain read/write operations and fault tolerance.
   • Features: scalability, high throughput.
7. 7 Marks
   aExplain in detail about HIVE.
   How to answer

   • Define HIVE and its role as data warehouse on Hadoop.
   • Explain architecture: HiveQL, metastore, execution engine.
   • Explain data models, tables and working with HDFS.
   • Advantages and use cases.
8. 7 Marks
   bWrite with suitable example the HIVE Queries for Natural join and Out-join.
   How to answer

   • Define Natural join and Outer join in HIVE context.
   • Give sample tables with schema and data.
   • Write HiveQL queries for NATURAL JOIN and LEFT/RIGHT/FULL OUTER JOIN.
   • Explain output of queries.
9. 7 Marks
   aDiscuss the various data types in Pig.
   How to answer

   • Introduce Pig and Pig Latin data model.
   • Explain scalar types: int, long, float, double, chararray, bytearray.
   • Explain complex types: Tuple, Bag, Map.
   • Give example declarations for each type.
10. 7 Marks
    bHow NoSQL is useful for Big data problems?
    How to answer

    • Define NoSQL and its characteristics vs RDBMS.
    • Explain suitability for Volume, Variety, Velocity.
    • Explain types of NoSQL and scalability, schema flexibility.
    • Examples of Big Data problems solved by NoSQL.
11. 7 Marks
    aExplain in detail about Market and Business drivers for Big data analytics.
    How to answer

    • Define market and business drivers for analytics.
    • Explain drivers: competition, customer personalization, operational efficiency.
    • Explain cost reduction, new business models, decision making.
    • Link drivers to Big Data adoption.
12. 7 Marks
    bExplain the process of Sharding in MongoDB.
    How to answer

    • Define sharding and its need in MongoDB.
    • Explain components: shards, mongos, config servers, shard key.
    • Explain steps of enabling sharding and data distribution.
    • Advantages for scalability and performance.
13. 7 Marks
    aWhich mining method is most frequently used for Social network graph? Explain.
    How to answer

    • Define social network graph mining.
    • Identify frequently used method e.g., community detection / link mining / frequent subgraph mining.
    • Explain steps of the method.
    • Give example application.
14. 7 Marks
    bWhat do you understand by clustering in Social network analysis?
    How to answer

    • Define clustering in social network analysis.
    • Explain purpose: community / group identification.
    • Brief clustering approaches for graphs.
    • Example and significance.
15. 14 Marks
    Write short notes on any two:

    1. i)RDBMS Vs Hadoop
    2. ii)Hadoop Ecosystem
    3. iii)ETL Processing
    4. iv)Variation of NOSAQL
    How to answer

    • Select any two topics from the four given options.
    • Define each selected topic with key features.
    • Explain architecture / process / comparison as applicable.
    • Give example / diagram and uses.