AKTU · B.Tech · Important questions
AKTU Data Analytics Important Questions
The questions AKTU asks again and again in Data Analytics, found by comparing every question in 10 previous year papers from 2020-21 to 2025-26 (subject codes BADS601, BCS052, BIT601, KCS051, KIT601).
How this list is made: we compared all 235 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.
- Given data= {2,3,4,5,6,7;1,5,3,6,7,8}. Compute the principal component using PCA algorithm.
- Given data = {1, 2, 3, 4; 4, 5, 6, 7}. Compute the principal components using the PCA algorithm.
- Explore PCA. Given data = {4, 8, 13, 7; 11, 4, 5, 14}. Compute the principal component using PCA algorithm. Also use PCA to reduce dimension from 2 to 1.
- Cluster the following eight points (with (x, y) representing locations) into three clusters: A1(2, 10), A2(2, 5), A3(8, 4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2), A8(4, 9) Initial cluster centers are A1(2, 10), A4(5, 8) and A7(1, 2). The distance function between two points a = (x1, y1) and b = (x2, y2) is defined as- Use K-Means Algorithm to find the three cluster centers after the second iteration
- Compare various types of support vector and kernel methods of data analysis.
- What are the different kernel methods of Data Analytics?
- Draw the architecture of HIVE with its features.
- Draw and discuss the architecture of Hive in detail.
- Describe the architecture of HIVE with its features.
- Explain various phases of Data Analytics Life Cycle.
- Develop and explain the data analytics life cycle
- Explain the various phases of data analytics life cycle.
- What is a neural network? How can it be used in analytics?
- Summarize the utility of Neural Network in Analytics.
- Differentiate between Neural Network and Artificial Neural Network.
- A= {(10,0.2), (20,0.4), (25,0.7), (30,0.9), (40,1), (50,0.4} B= {(10,0.4), (20,0.1), (25,0.9), (30,0.2), (40,0.6), (50, 0.6)} Apply Union, Intersection, Complement, Bold Union and Bold Intersection operations on above listed Fuzzy Sets.
- Discuss Fuzzy membership function. Apply Union, Intersection, Complement, Bold Union and Bold Intersection operations on fuzzy sets A and B. Given: A = {(10,0.2), (20,0.4), (25,0.7), (30,0.9), (40,1.0), (50,0.4)} B = {(10,0.4), (20,0.1), (25,0.9), (30,0.2), (40,0.6), (50,0.6)}
- Brief about the main components of MapReduce
- Brief about the working of CLIQUE algorithm.
- Explain the working of CLIQUE algorithm in brief.
- Briefly discuss the steps in query processing.
- Identify the major issues in data stream query processing.
- Describe the process of mining frequent item sets from an FP-Tree.
- Describe the Apriori algorithm for mining frequent item sets. How does it work and what are its main steps?
- Differentiate between CLIQUE and ProCLUS clustering
- Discuss the concept of filtering data stream processing. Explain Bloom filtering in detail.
- Discuss the Concept of filtering in Data Stream Processing. Explain Bloom Filtering in detail.
- Explain Apriori association rule mining algorithm.
- Discuss market basket analysis and association rule mining.
- Explain any one algorithm to count number of distinct elements in a data stream.
- Explain multivariate analysis and Bayesian network.
- Discuss any two techniques used for multivariate analysis.
- Explain the Data Analytics Lifecycle in detail with a neat diagram.
- Explain the phases of the Data Analytics Lifecycle with objectives of each phase.
- Explain the architecture of data stream model.
- Design and explain the architecture of data stream model.
- Illustrate various Real Time Analytics Platform s (RTAP s ) with examples .
- What is a Real-Time Analytics Platform (RTAP)? Discuss some applications of RTAP in industries today.
- List the advantages and disadvantages of K-Means clustering.
- Compare hierarchical clustering and K-means clustering with suitable applications.
- Provide an in-depth comparison between the CLIQUE and ProCLUS clustering algorithms. How do these methods handle challenges such as noise, outliers, and varying cluster shapes?
- Provide an in-depth comparison between the CLIQUE and ProCLUS 10 clustering algorithms. How do these methods handle challenges such as noise, outliers, and varying cluster shapes?
- What happens in the ‘model planning’ and ‘model building’ stages? How are these two different?
- What is Prediction error ? With the help of suitable example explain prediction error in classification and regression.
- What is prediction error? State and explain the prediction error in regression and classification with suitable example.
- Write R function to check whether the given number is prime or not?
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Define multivariate analysis.
- Explain multivariate analysis.
- What is multivariate analysis?
- Enumerate sampling data in a stream.
- What is a data stream?
- What is the role of sampling data in a stream? 3
- Write names of two visualization tools.
- List the names of any 2 visualization tools.
- Explain the principle behind Hierarchical clustering technique.
- What is the principle behind hierarchical clustering technique?
- Give the full form of RTAP.
- Give the full form of RTAP and discuss its application.
- How can you deal with uncertainty?
- List any two characteristics of Big Data.
- What are the main characteristics of Big Data?
- List the phases of the Data Analytics Lifecycle.
- List various phases of data analytics lifecycle.
- What are the main sources of data in data analytics?
- What are the sources of data in the context of data analytics?
- What is Data Analytics?
- Discuss the need of data analytics.
- What is decision tree?
- Explain decision tree.
- What is regression analysis?
- What are the purposes of regression analysis?
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BIT601, 2025-26 BCS052, 2024-25 BIT601), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Explain Support Vector Machines (SVM) with the concept of margin and support vectors. (2025-26, BCS052)
- A dataset has eigenvalues: 4.5, 2.8, 1.2, 0.4, 0.1. (i) Calculate percentage variance of each component. (ii) Determine the number of components required to retain 90% variance. (2025-26, BCS052)
- What happens in the ‘model planning’ and ‘model building’ stages? How are these two different? (2025-26, BIT601)
- Discuss the evolution of Big Data and need for data analytics in modern organizations. (2025-26, BIT601)
- What is the difference between data analysis and data reporting? Give examples to show how each is used. (2024-25, BIT601)
- What happens in the ‘model planning’ and ‘model building’ stages? How are these two different? (2024-25, BIT601)
Unit 2
- A sliding window of size 200 slides by 50 elements. Calculate the number of windows formed after processing 1,000 elements and explain its impact on latency. (2025-26, BCS052)
- Design a Real-Time Analytics Platform for social media sentiment analysis showing major components. (2025-26, BCS052)
- Explain neural networks including learning, generalization, and competitive learning. (2025-26, BIT601)
- Discuss Fuzzy membership function. Apply Union, Intersection, Complement, Bold Union and Bold Intersection operations on fuzzy sets A and B. Given: A = {(10,0.2), (20,0.4), (25,0.7), (30,0.9), (40,1.0), (50,0.4)} B = {(10,0.4), (20,0.1), (25,0.9), (30,0.2), (40,0.6), (50,0.6)} (2025-26, BIT601)
- What are neural networks? Explain how learning and generalization occur in neural networks. Include a simple diagram if possible. (2024-25, BIT601)
- Given data = {1, 2, 3, 4; 4, 5, 6, 7}. Compute the principal components using the PCA algorithm. (2024-25, BIT601)
Unit 3
- Given transactions: T1 {A, B, C}, T2 {A, B}, T3 {A, C}, T4 {B, C}, T5 {A, B, C}. If minimum support = 60%, find all frequent item sets and show pruning. (2025-26, BCS052)
- Compare hierarchical clustering and K-means clustering with suitable applications. (2025-26, BCS052)
- Discuss Real-Time Analytics Platforms (RTAP) and explain applications like sentiment analysis and stock prediction. (2025-26, BIT601)
- Apply the Flajolet–Martin algorithm on the following data stream to estimate the number of unique elements: 𝑆 ൌ ሼ1,3,2,1,2,3,4,3,1,2,3,1ሽ Given the hash function: ℎሺ𝑥ሻ ൌ ሺ3𝑥 1ሻ 𝑚𝑜𝑑 8 Compute the hash values for each element i. Represent the hash values in 3-bit binary form ii. Determine the maximum number of trailing zeros (R) iii. Estimate the number of distinct elements using 2 (2025-26, BIT601)
- What is a Real-Time Analytics Platform (RTAP)? Discuss some applications of RTAP in industries today. (2024-25, BIT601)
- Explain the concept of sampling in data streams. Why is it necessary, and how does it help in data analysis? (2024-25, BIT601)
Unit 4
- Explain the working of MapReduce with intermediate key–value pairs. (2025-26, BCS052)
- Design a Hadoop-based framework for storing and processing large-scale data. (2025-26, BCS052)
- Discuss market basket analysis and association rule mining. (2025-26, BIT601)
- Cluster the following eight points (with (x, y) representing locations) into three clusters: A1(2, 10), A2(2, 5), A3(8, 4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2), A8(4, 9) • Initial cluster centers are: A1(2, 10), A4(5,8) and A7(1, 2). • The distance function between two points a = (x1, y1) and b = (x2, y2) is defined as 𝑑ሺ𝑎, 𝑏ሻ ൌ |𝑥2 – 𝑥1| |𝑦2 – 𝑦1| Use K-Means Algorithm to find the three cluster centers after implanting all eight points. (2025-26, BIT601)
- Why is parallelism important in clustering large datasets? Describe some techniques used to achieve parallel clustering. (2024-25, BIT601)
- What is Pro CLUS? How is it different from CLIQUE in handling clustering? (2024-25, BIT601)
Unit 5
- For the dataset {12, 15, 20, 22, 25, 30}, calculate mean and standard deviation and comment on data spread. (2025-26, BCS052)
- Design an R-based workflow for analyzing unstructured text data. (2025-26, BCS052)
- Explain MapReduce model with architecture and working in Apache Hadoop. (2025-26, BIT601)
- Explain Exploratory Data Analysis (EDA) in R with steps and sample commands. (2025-26, BIT601)
- What is sharding in NoSQL databases? Why is it important for handling large volumes of data? (2024-25, BIT601)
- What is the Hadoop Distributed File System (HDFS)? How does it handle data storage and fault tolerance? (2024-25, BIT601)
Papers used for this list
- AKTU Data Analytics (BCS052) question paper 2025-26
- AKTU Data Analytics (BIT601) question paper 2025-26
- AKTU Data Analytics (BADS601) question paper 2024-25
- AKTU Data Analytics (BCS052) question paper 2024-25
- AKTU Data Analytics (BIT601) question paper 2024-25
- AKTU Data Analytics (KCS051) question paper 2023-24
- AKTU Data Analytics (KCS051) question paper 2022-23
- AKTU Data Analytics (KIT601) question paper 2022-23
- AKTU Data Analytics (KIT601) question paper 2021-22
- AKTU Data Analytics (KCS051) question paper 2020-21
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 Data 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 235 questions from 10 AKTU Data Analytics papers (2020-21 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 Data 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