AL-802-C Big Data Analytics - 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 concept of Big Data and its significance in today's digital world.
   How to answer

   • Define Big Data with Vs (Volume, Velocity, Variety, Veracity, Value).
   • Explain sources of data generation in digital world.
   • Discuss significance: decision making, business intelligence, science, governance.
   • Give examples of applications.
2. 7 Marks
   bDiscuss the major challenges associated with handling Big Data, including data storage, processing, security and governance.
   How to answer

   • Outline storage challenge: volume, scalability.
   • Processing challenge: velocity, variety, real-time needs.
   • Security, privacy and governance issues.
   • Mention cost, skills, data quality challenges.
3. 7 Marks
   aHow is data analytics applied to Big Data? Discuss the role of predictive analytics, machine learning and real-time data processing.
   How to answer

   • Explain application of analytics to Big Data lifecycle.
   • Role of predictive analytics with examples.
   • Role of machine learning for pattern discovery.
   • Role of real-time processing and stream analytics.
4. 7 Marks
   bProvide an overview of the technologies available for managing and analyzing Big Data, such as Hadoop, Spark and NoSQL databases.
   How to answer

   • Overview of Hadoop ecosystem for storage and batch processing.
   • Overview of Spark for in-memory and fast processing.
   • Overview of NoSQL databases for flexible storage.
   • Comparison of use-cases for each technology.
5. 7 Marks
   aDescribe the core components of Hadoop, including HDFS, MapReduce and YARN. What are their respective functions?
   How to answer

   • Define HDFS and its master-slave architecture and functions.
   • Define MapReduce and its processing function.
   • Define YARN and its resource management function.
   • Show interaction among the three components.
6. 7 Marks
   bCompare RDBMS and Hadoop in terms of data processing, storage, scalability and performance. In what scenarios is Hadoop preferred over RDBMS.
   How to answer

   • Compare data model, storage and schema approach.
   • Compare scalability and performance characteristics.
   • Compare processing paradigm: SQL vs distributed batch.
   • Identify scenarios where Hadoop is preferred.
7. 7 Marks
   aExplain the MapReduce programming model. How does it work and what are its key phases in data processing?
   How to answer

   • Define MapReduce programming model and philosophy.
   • Explain working with Map, Shuffle/Sort, Reduce phases.
   • Describe key phases and data flow with example.
   • Mention advantages and limitations.
8. 7 Marks
   bExplain the concept of Apache Hive and its role in data warehousing. How does it simplify querying large datasets in Hadoop?
   How to answer

   • Define Apache Hive and data warehousing on Hadoop.
   • Explain HiveQL, metastore and translation to MapReduce/Tez/Spark.
   • Explain schema-on-read and querying large datasets.
   • Give example use-case for SQL-like querying.
9. 7 Marks
   aIntroduce Apache Pig and explain how it differs from Hive. What are the key advantages of using Pig for data processing?
   How to answer

   • Introduce Apache Pig and Pig Latin.
   • Differentiate Pig vs Hive on language, use-case, users.
   • List advantages: procedural, ETL, rapid prototyping.
   • Give example scenario for Pig use.
10. 7 Marks
    bDiscuss the process of ETL (Extract, Transform, Load) using Pig. How does Pig facilitate large-scale data transformation?
    How to answer

    • Define ETL steps: Extract, Transform, Load.
    • Explain Pig Latin operators for ETL workflow.
    • Explain how Pig handles large-scale transformation.
    • Mention execution on MapReduce and optimization.
11. 7 Marks
    aDiscuss different NoSQL data architectural patterns. How do they help in handling large-scale and unstructured data?
    How to answer

    • List NoSQL architectural patterns: key-value, document, column, graph.
    • Explain sharding, replication, eventual consistency.
    • Explain handling of large-scale unstructured data.
    • Give examples of pattern usage.
12. 7 Marks
    bDescribe the main types of NoSQL databases (Key-Value, Document, Column-Family and Graph). Provide examples of each type.
    How to answer

    • Describe Key-Value, Document, Column-Family, Graph stores.
    • Explain characteristics and data model of each.
    • Provide examples: Redis, MongoDB, Cassandra, Neo4j.
    • Mention suitable use-cases for each type.
13. 7 Marks
    aDiscuss the data model of MongoDB. How does it store and manage data using JSON-like documents?
    How to answer

    • Explain MongoDB document data model and BSON.
    • Describe collections, documents, dynamic schema.
    • Explain storage, indexing and management features.
    • Give JSON-like document example.
14. 7 Marks
    bDiscuss various applications of social network mining in domains such as marketing, healthcare and cybersecurity.
    How to answer

    • Define social network mining.
    • Discuss marketing applications: targeting, sentiment.
    • Discuss healthcare and cybersecurity applications.
    • Mention community and influence analysis uses.
15. 7 Marks
    aDiscuss the role of graph-based machine learning techniques in social network mining. How do they improve community detection and recommendation?
    How to answer

    • Define graph-based ML: GNNs, node embeddings.
    • Explain role in modeling social graph structure.
    • Explain improvement in community detection.
    • Explain improvement in recommendation/link prediction.
16. 7 Marks
    bCompare collaborative filtering and content-based filtering in recommender systems. How do they apply to social networks.
    How to answer

    • Define collaborative filtering and content-based filtering.
    • Compare on basis: data used, cold-start, scalability.
    • Explain application to social networks with examples.
    • Mention hybrid approaches.