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Previous year question hub

Data Science & Artificial Intelligence Previous Year Questions

Practice Data Science & Artificial Intelligence previous year questions organised from real papers, with year-wise coverage and clear topic navigation.

3Papers
3Years
163Questions
7Topics

Data Science & Artificial Intelligence question pattern

Every graph below is calculated only from this selection.

Questions by year

Year-wise coverage for Data Science & Artificial Intelligence. Each bar uses a separate theme-derived color.

Difficulty distribution

How the classified questions are distributed by difficulty.

Easy 86 52.8%
Medium 73 44.8%
Hard 4 2.5%

Question type distribution

MCQ, numerical, multiple-select and other formats found in these papers.

MCQ 76 46.6%
Numerical Answer Type (NAT) 46 28.2%
MSQ 41 25.2%

Subject weightage

Top subjects by unique question coverage.

Data Science & Artificial Intelligence
163 Qs

Most asked topics

Top topics across the included previous year papers.

Probability and Statistics
37 Qs
Programming, Data Structures and Algorithms
30 Qs
Database Management and Warehousing
22 Qs
Linear Algebra
21 Qs
Machine Learning
21 Qs
AI
20 Qs
Calculus and Optimization
12 Qs

Subtopic coverage

Top subtopics inside this exact selection.

Random Variables and Distributions
23 Qs
Supervised Learning
17 Qs
Python Programming and Data Structures
13 Qs
Single-variable Differential Calculus
12 Qs
Eigenvalues, Projections and Quadratic Forms
11 Qs
Relational Databases and SQL
11 Qs
Probability Rules and Conditional Probability
10 Qs
Searching, Sorting and Divide-and-conquer
10 Qs
Normalization, Storage and Indexing
8 Qs
Vector Spaces and Matrix Properties
8 Qs
Graphs, Traversals and Shortest Paths
7 Qs
Informed, Uninformed and Adversarial Search
7 Qs
Propositional and Predicate Logic
7 Qs
Reasoning under Uncertainty
6 Qs
Unsupervised Learning
4 Qs
Descriptive Statistics and Correlation
3 Qs
Data Transformation and Warehousing
3 Qs
Linear Systems and Matrix Decompositions
2 Qs
Limit Theorems and Statistical Inference
1 Qs

Paper coverage

Question coverage for the most populated papers. Every active PYP paper remains listed below.

Data Science and Artificial Intelligence (DA) 2026
55 Qs
Data Science & Artificial Intelligence (DA) 2025
55 Qs
Data Science & Artificial Intelligence (DA) 2024
53 Qs

Browse by topics

Open a focused page built from the same verified paper data.

Probability and Statistics

Explore previous-paper coverage, trends and focused practice for Probability and Statistics.

37 questions3 papers3 years

Programming, Data Structures and Algorithms

Explore previous-paper coverage, trends and focused practice for Programming, Data Structures and Algorithms.

30 questions3 papers3 years

Database Management and Warehousing

Explore previous-paper coverage, trends and focused practice for Database Management and Warehousing.

22 questions3 papers3 years

Linear Algebra

Explore previous-paper coverage, trends and focused practice for Linear Algebra.

21 questions3 papers3 years

Machine Learning

Explore previous-paper coverage, trends and focused practice for Machine Learning.

21 questions3 papers3 years

AI

Explore previous-paper coverage, trends and focused practice for AI.

20 questions3 papers3 years

Calculus and Optimization

Explore previous-paper coverage, trends and focused practice for Calculus and Optimization.

12 questions3 papers3 years

Included previous year papers

Newest papers appear first. Sort by year, question coverage or name.

PaperYear / sessionQuestions in this viewOpen
Data Science and Artificial Intelligence (DA) 2026202655View paper
Data Science & Artificial Intelligence (DA) 2025202555View paper
Data Science & Artificial Intelligence (DA) 2024202453View paper

Sample previous year questions

A varied preview from the papers represented in this selection, with every available option.

1
2024 · Data Science & Artificial Intelligence · Probability and Statistics · Random Variables and Distributions
Data Science & Artificial Intelligence (DA) 2024
Consider the following statements:
(i) The mean and variance of a Poisson random variable are equal.
(ii) For a standard normal random variable, the mean is zero and the variance is one.
Which ONE of the following options is correct?
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2
2025 · Data Science & Artificial Intelligence · Probability and Statistics · Probability Rules and Conditional Probability
Data Science & Artificial Intelligence (DA) 2025
Suppose \(X\) and \(Y\) are random variables. The conditional expectation of \(X\) given \(Y\) is denoted by \(E[X|Y]\). Then \(E[E[X|Y]]\) equals
Open complete paper
3
2026 · Data Science & Artificial Intelligence · Linear Algebra · Eigenvalues, Projections and Quadratic Forms
Data Science and Artificial Intelligence (DA) 2026
For a classification problem, Principal Component Analysis (PCA) has been used to reduce the dimensionality of a feature space from 100 to 10.
Which of the following options is true about the angle \( \theta \) between the first and the tenth principal components?
Open complete paper
4
2024 · Data Science & Artificial Intelligence · Probability and Statistics · Probability Rules and Conditional Probability
Data Science & Artificial Intelligence (DA) 2024
Three fair coins are tossed independently. \( T \) is the event that two or more tosses result in heads. \( S \) is the event that two or more tosses result in tails.
What is the probability of the event \( T \cap S \)?
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5
2025 · Data Science & Artificial Intelligence · Linear Algebra · Linear Systems and Matrix Decompositions
Data Science & Artificial Intelligence (DA) 2025
The number of additions and multiplications involved in performing Gaussian elimination on any \(n \times n\) upper triangular matrix is of the order
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6
2026 · Data Science & Artificial Intelligence · Machine Learning · Supervised Learning
Data Science and Artificial Intelligence (DA) 2026
Consider that you are training a classifier for a 10-class classification problem. Each input is represented as a 512-dimensional vector. There are 1000 samples, out of which first 100 will be used for testing. Let Leave-One-Out-Cross-Validation (LOOCV) be used for selection of the classifier model before testing. Which of the following options is the correct number of validation splits that will be generated?
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