ME-803-A Data Analytics - May 2023 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
   aWhat is Big Data? Explain the characteristics of Big Data?
   How to answer

   • Define Big Data as large, complex datasets beyond traditional processing.
   • Explain characteristics - Volume, Velocity, Variety, Veracity, Value.
   • Give examples of sources and need for analytics.
2. 7 Marks
   bDifferentiate between Regression and ANOVA.
   How to answer

   • Define Regression - predicting continuous dependent variable.
   • Define ANOVA - comparing means across groups.
   • Compare on purpose, data type, output and use cases in table.
3. 7 Marks
   aExplain with and example how Big Data analytics can be useful in development of smart cities?
   How to answer

   • Define smart city data sources - IoT, traffic, energy, waste.
   • Explain analytics uses - traffic management, energy optimization, safety.
   • Give concrete example e.g. traffic prediction or smart lighting.
4. 7 Marks
   bWrite down the goals of HDFS.
   How to answer

   • Define HDFS as Hadoop distributed storage.
   • List goals - fault tolerance, large datasets, streaming access.
   • Mention portability, scalability and commodity hardware.
5. 7 Marks
   aDefine term frequency and inverse document frequency.
   How to answer

   • Define term frequency (TF) with formula.
   • Define inverse document frequency (IDF) with formula.
   • Explain use in TF-IDF weighting for text mining.
6. 7 Marks
   bExplain the 4 V's of Big Data.
   How to answer

   • List 4 V's - Volume, Velocity, Variety, Veracity.
   • Explain each with example.
   • Mention significance for Big Data systems.
7. 7 Marks
   aExplain the architecture and features of Hive.
   How to answer

   • Describe Hive architecture - UI, Driver, Compiler, Metastore, Execution Engine.
   • Explain workflow of HiveQL to MapReduce.
   • List features - data warehousing, HiveQL, scalability.
8. 7 Marks
   bExplain the concept of metastore in Hive.
   How to answer

   • Define metastore as central repository of Hive metadata.
   • Explain types - embedded, local, remote.
   • Explain role in schema, partition and table management.
9. 7 Marks
   aWhat is Pig Latin and why do we need it?
   How to answer

   • Define Pig Latin as dataflow scripting language for Pig.
   • Explain need - simplifies MapReduce, handles semi-structured data.
   • Give example of Pig Latin statements.
10. 7 Marks
    bExplain Map Reduce programming model.
    How to answer

    • Define Map and Reduce phases.
    • Explain steps - split, map, shuffle, sort, reduce.
    • Give word-count example and advantages.
11. 7 Marks
    aExplain Hadoop Architecture with diagram and components.
    How to answer

    • Draw Hadoop architecture with NameNode, DataNode, ResourceManager.
    • Explain HDFS and YARN layers.
    • List components and their functions.
12. 7 Marks
    bWhat is Zoo keeper? Write the advantages of Zoo Keeper.
    How to answer

    • Define ZooKeeper as coordination service for distributed apps.
    • Explain functions - configuration, synchronization, leader election.
    • List advantages - reliability, scalability, high availability.
13. 7 Marks
    aExplain any three Hive QL DDL commands with syntax and example.
    How to answer

    • List any three DDL commands e.g. CREATE DATABASE, CREATE TABLE, ALTER, DROP.
    • Write syntax for each.
    • Give example query with output effect.
14. 7 Marks
    bExplain the Indexing and Aggregation for Mongo DB.
    How to answer

    • Define indexing in MongoDB - single, compound, geospatial.
    • Explain aggregation pipeline stages - match, group, sort.
    • Give example queries for both.
15. 14 Marks
    Write short notes on any three of the following:

    1. a)Capacity scheduler in Map Reduce
    2. b)Information Management
    3. c)Metastore in Hive
    4. d)Term frequency
    How to answer

    • Select any three topics from list.
    • Give definition, key points and use case for each.
    • Conclude with applications in Big Data ecosystem.