AKTU Big Data and Analytics (KDS-601) Question Paper 2022-23
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow |
|---|---|
| Course | B.Tech, Semester VI |
| Subject | Big Data and Analytics (KDS-601) |
| Exam | Theory Examination 2022-23 (even semester, held June 2023) |
| Time · Marks | 3 hours · 100 marks |
| Source | AKTU question paper, via Notes Gallery |
Note: Attempt all sections. If you require any missing data, then choose suitably.
Section A
Q1. Attempt all questions in brief · 2 × 10 = 20 marks
- List out the different kind of Digital Data. [2]
- Write short note on Drivers of Big Data. [2]
- How Data format of Hadoop is important? [2]
- Explain Distributed File System. [2]
- How file system Works? [2]
- Discuss the use of Data Replication. [2]
- Why Scheduler is required in Hadoop System? [2]
- List the Data Type used in Mango DB. [2]
- Write the Applications of Big Data using Pig. [2]
- List out the Data Processing Operators used in Pig. [2]
Section B
Q2. Attempt any three · 10 × 3 = 30 marks
- How to overcome the Challenges of Conventional System in Data Analysis? [10]
- Explain the concept of Hadoop Echo System with proper diagram and example. [10]
- Illustrate the concept of HDFS Monitoring and Maintenance Process. [10]
- Discuss the New Features implemented in Hadoop 2.0 Version. [10]
- Explain the Process of Apache Hive installation Process and its Architecture in detail. [10]
Section C
Q3 to Q7. Attempt any one part of each question · 10 × 5 = 50 marks
Q3
- Explain about the Big Data Architecture and its Characteristics. [10]
- Discuss the various Big Data Features in terms of Security, Protection and Auditing. [10]
Q4
- Discuss the Working of Map Reduce and its Characteristics. [10]
- Explain the various Job Scheduling Techniques used in the Map Reduce. [10]
Q5
- Write the Benefits and Challenges of Hadoop Distributed File System in detail. [10]
- Discuss the Security issues in Hadoop and why it is important for Data analysis? [10]
Q6
- Explain the Various Ecosystem Components used in Hadoop with proper example. [10]
- Define Inheritance. Explain the Built in Control Structure of SCALA. [10]
Q7
- Explain the Process of building the applications with Zookeeper. [10]
- Define Infosphere. Explain the various IBM Big Data Strategies used now a days. [10]
Most repeated topics
How many of the last 3 AKTU 6th-semester Big Data and Analytics papers (KDS-601 2022-23, KDS601 2023-24, BCDS601 2024-25) asked about each topic.
Counted by LearnAIFast from the 2022-23, 2023-24 and 2024-25 even-semester theory papers.
Answer hints
Q1(b). Drivers of Big Data
Cheap storage and cloud computing, the growth of smartphones, social media and IoT sensors, and businesses wanting faster, data-driven decisions. Link them to the 3 Vs: Volume, Velocity and Variety.
Q2(d). New features in Hadoop 2.0
YARN separates resource management from processing, so engines other than MapReduce (Spark, Tez) can run on the cluster. Other features: NameNode High Availability with a standby NameNode, HDFS Federation (several NameNodes sharing the cluster), HDFS snapshots, and better scalability to thousands of nodes.
Q4(a). Working of MapReduce
Input is split into blocks. The Map phase turns each record into intermediate key–value pairs. Shuffle and sort groups all values with the same key and sends them to one reducer. The Reduce phase combines each group into the final output. Use word count as the example: map emits (word, 1), reduce sums the counts. Characteristics: parallel, fault tolerant, moves computation to the data.
Q5(a). Benefits and challenges of HDFS
Benefits: stores very large files across cheap machines, fault tolerance through block replication (default 3 copies), high throughput for batch reads, and data locality. Challenges: slow for many small files, not suited to low-latency access, files are write-once (append only), and the NameNode was a single point of failure before Hadoop 2.
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