AKTU · B.Tech · Important questions
AKTU Big Data and Analytics Important Questions
The questions AKTU asks again and again in Big Data and Analytics, found by comparing every question in 4 previous year papers from 2022-23 to 2025-26 (subject codes BCDS601, KDS601).
How this list is made: we compared all 94 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.
Most repeated long questions
From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.
- Differentiate between NoSQL and RDBMS. Discuss the key characteristics of NoSQL databases.
- Differentiate NoSQL databases from traditional RDBMS. Also list the key characteristics and use cases for NoSQL databases.
- Discuss Hadoop Distributed File System (HDFS), and how does it work.
- Write the Benefits and Challenges of Hadoop Distributed File System in detail.
- Discuss the Security issues in Hadoop and why it is important for Data analysis.
- Discuss the Working of Map Reduce and its Characteristics.
- Explain the various Job Scheduling Techniques used in the Map Reduce.
- Discuss the various Big Data Features in terms of Security, Protection and Auditing.
- Explain the Various Ecosystem Components used in Hadoop with proper example.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- How Data format of Hadoop is important?
- Explain how the data format of Hadoop is important.
- How file system Works?
- Define how file system Works.
- List out the Data Processing Operators used in Pig.
- List the Data Type used in Mango DB.
- Write short note on Drivers of Big Data.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BCDS601, 2024-25 BCDS601, 2023-24 KDS601), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Explain the Big Data Technology Stack. Discuss the challenges of conventional systems. (2025-26, BCDS601)
- Compare and contrast Analysis vs. Reporting. Discuss modern data analytic tools. (2025-26, BCDS601)
- Describe the importance and applications of Big Data in different industries such as healthcare, finance, and e-commerce. How does Big Data contribute to decision-making in these sectors? (2024-25, BCDS601)
- What are the major concerns related to Big Data privacy and ethics? Discuss how auditing and compliance play a role in addressing these concerns. (2024-25, BCDS601)
- Distinguishes Big Data analytics from traditional data analytics. Also list the techniques and tools used in Big Data analytics. (2023-24, KDS601)
- Discuss the various Big Data Features in terms of Security, Protection and Auditing. (2023-24, KDS601)
Unit 2
- Describe the process of Data Ingestion using Flume and Sqoop. (2025-26, BCDS601)
- Explain the HDFS Read and Write paths. How does the client interact with the NameNode? (2025-26, BCDS601)
- Discuss the process of job scheduling and task execution in Hadoop. How does Hadoop ensure fault tolerance during task execution? (2024-25, BCDS601)
- What are HDFS block size and block abstraction? How do they contribute to performance in Big Data systems (2024-25, BCDS601)
- Discuss the roles of additional components like HBase, Pig, and Hive in the Hadoop ecosystem. (2023-24, KDS601)
- Discuss the Working of Map Reduce and its Characteristics. (2023-24, KDS601)
Unit 3
- Explain the querying mechanism in MongoDB. How does it handle complex data structures compared to traditional SQL queries? (2025-26, BCDS601)
- Compare and contrast the Schema-less nature of NoSQL databases with the Schema-oriented nature of traditional RDBMS. Discuss the advantages of using NoSQL for Big Data. (2025-26, BCDS601)
- Design a data ingestion and transformation pipeline for a real-time social media analytics system using tools like Flume, Sqoop, and Avro. (2024-25, BCDS601)
- Explain the architecture and use cases of Apache Flume and Apache Sqoop in Big Data systems. How do these tools support efficient data ingestion from structured and unstructured sources. (2024-25, BCDS601)
- Explain how HDFS implement data replication and list its advantages. (2023-24, KDS601)
- Discuss the Security issues in Hadoop and why it is important for Data analysis. (2023-24, KDS601)
Unit 4
- Analyze the Resilient Distributed Dataset (RDD) model in Spark. How does Spark's "In-Memory" processing provide a performance advantage over the traditional MapReduce disk-based approach? (2025-26, BCDS601)
- Discuss the importance of closures and higher-order functions in Scala. How do these features simplify the development of distributed processing logic for Spark? (2025-26, BCDS601)
- Evaluate the use of NoSQL databases in real-time big data applications. Discuss their strengths and weaknesses compared to SQL databases. (2024-25, BCDS601)
- Evaluate the performance benefits and limitations of Spark over traditional MapReduce in big data processing. (2024-25, BCDS601)
- Explain how documents can be created, updated, and deleted in MongoDB. (2023-24, KDS601)
- Explain the Various Ecosystem Components used in Hadoop with proper example. (2023-24, KDS601)
Unit 5
- Compare Local Mode and MapReduce Mode in Apache Pig. In what specific scenarios would a developer choose the Grunt shell over a pre- written Pig script? (2025-26, BCDS601)
- Analyze why HBase is superior for random, real-time read/write access to Big Data compared to HDFS alone or a traditional RDBMS. Discuss its Schema Design and Advanced Indexing capabilities. (2025-26, BCDS601)
- Describe HBase and its data model. How does HBase differ from traditional relational databases, and what types of applications benefit most from using HBase (2024-25, BCDS601)
- Explain the process of data loading and querying in Hive. How does HiveQL differ from standard SQL, and what are its advantages (2024-25, BCDS601)
- Explain the IBM's overall strategy for Big Data, and its key components. (2023-24, KDS601)
- Discuss Big SQL, and how it extends SQL capabilities to Big Data environments. (2023-24, KDS601)
Papers used for this list
- AKTU Big Data and Analytics (BCDS601) question paper 2025-26
- AKTU Big Data and Analytics (BCDS601) question paper 2024-25
- AKTU Big Data and Analytics (KDS601) question paper 2023-24
- AKTU Big Data and Analytics (KDS601) question paper 2022-23
How to use these questions
- Start with the questions asked in the most papers, and make short notes for each.
- Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
- Revise all the 2-mark questions the night before the exam.
- Finish with one full paper from the list above, timed.
Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.
Frequently asked questions
Are these AKTU Big Data and Analytics questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.
How was this important questions list made?
We compared all 94 questions from 4 AKTU Big Data and Analytics papers (2022-23 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.
Where can I download AKTU Big Data and Analytics previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.
More subjects: all AKTU important questions · all question papers