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raccourcis clavier

Exam Pattern & Syllabus

Exam Overview

ParameterDetails
Duration3 Hours
Total Marks100
Total Questions65
General Aptitude (GA)10 Questions — 15 Marks
Core DA/AI Subjects55 Questions — 85 Marks
Question TypesMCQ, MSQ, NAT

Marking Scheme

  • 1-mark MCQ: −1/3 for an incorrect answer

  • 2-mark MCQ: −2/3 for an incorrect answer

  • MSQ: As per the question-specific marking scheme

  • NAT: No negative marking

Section 1: General Aptitude (GA)

Marks: 15
Questions: 10

Topics

  • Basic arithmetic

  • Percentages

  • Ratio & proportion

  • Analytical reasoning

  • Simple probability

  • Numerical reasoning

  • Verbal reasoning

  • Quantitative aptitude

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — Mean

Find the mean of the dataset:

{2,5,7,10}{2,5,7,10}

NAT — Speed, Distance & Time

A train leaves at 9:00 AM at a speed of 60 km/h. How far will it travel in 2.5 hours?

Section 2: Probability & Statistics

Marks: 15–20
Questions: 10

Topics

  • Counting

  • Permutations and combinations

  • Probability axioms

  • Sample space

  • Events

  • Conditional probability

  • Bayes’ theorem

  • Random variables

  • Probability distributions

  • Mean

  • Median

  • Mode

  • Variance

  • Standard deviation

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — Permutation

How many ways can 5 distinct books be arranged?

  • A. 60

  • B. 120

  • C. 24

  • D. 10

NAT — Probability

Given:

P(A)=0.4,P(B)=0.5,P(A∩B)=0.2P(A)=0.4,\quad P(B)=0.5,\quad P(A\cap B)=0.2

Find:

P(A∪B)P(A\cup B)

Section 3: Linear Algebra

Marks: 10–15
Questions: 8

Topics

  • Vectors and vector spaces

  • Subspaces

  • Linear independence and dependence

  • Matrices

  • Orthogonal matrices

  • Idempotent matrices

  • Partition matrices

  • Quadratic forms

  • Eigenvalues

  • Eigenvectors

  • Systems of linear equations

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MSQ — Eigenvalues

Identify all eigenvalues of:

A=[2003]A= \begin{bmatrix} 2 & 0\ 0 & 3 \end{bmatrix}

NAT — System of Linear Equations

Solve:

x+y=5x+y=5 2x−y=12x-y=1

Section 4: Calculus & Optimization

Marks: 10
Questions: 6

Topics

  • Functions

  • Limits

  • Continuity

  • Differentiability

  • Taylor series expansion

  • Maxima and minima

  • Single-variable optimization

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — Differentiation

Find the derivative of:

f(x)=x3−5x2+6f(x)=x^3-5x^2+6

NAT — Optimization

Find the maximum value of:

f(x)=4x−x2f(x)=4x-x

in the interval:

[0,5][0,5]

Section 5: Programming, Data Structures & Algorithms

Marks: 10–15
Questions: 10

Topics

Programming

  • Python programming

  • Python syntax and semantics

  • Functions

  • Data structures

  • Basic problem solving

Data Structures

  • Stacks

  • Queues

  • Linked lists

  • Trees

  • Hash tables

Searching & Sorting

  • Linear search

  • Binary search

  • Selection sort

  • Insertion sort

  • Merge sort

  • Quick sort

Graph Algorithms

  • Graph representation

  • BFS

  • DFS

  • Shortest path

  • Basic graph theory

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — Quick Sort

What is the worst-case time complexity of Quick Sort?

NAT — Python

Find the output of:

sorted([3, 1, 4, 2])

Section 6: Database Management & Data Warehousing

Marks: 5–10
Questions: 5–7

Topics

Database Fundamentals

  • ER model

  • Relational model

  • Relational algebra

  • Tuple relational calculus

SQL

  • SQL queries

  • Aggregate functions

  • Joins

  • Subqueries

  • Grouping

  • Filtering

Database Design

  • Integrity constraints

  • Functional dependencies

  • Normal forms

  • Indexing

Data Warehousing

  • Introduction to data warehousing

  • Basic data warehouse concepts

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — SQL

What is the output of:

SELECT COUNT(*)
FROM Employees
WHERE Salary > 50000;

NAT — Functional Dependency

Define/identify the functional dependency:

EmpID→EmpNameEmpID \rightarrow EmpName

Section 7: Machine Learning

Marks: 10–15
Questions: 8–10

Topics

Supervised Learning

  • Linear regression

  • Logistic regression

  • Ridge regression

  • Support Vector Machines (SVM)

Unsupervised Learning

  • K-Means clustering

  • Clustering techniques

  • Dimensionality reduction

Classification

  • Decision trees

  • Naive Bayes

  • K-Nearest Neighbors (KNN)

Neural Networks

  • Basic neural network concepts

  • Training

  • Weight updates

  • Gradient descent

Evaluation Metrics

  • Accuracy

  • Precision

  • Recall

  • F1-score

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — Regression

Which algorithm can be used for regression prediction?

NAT — Gradient Descent

Compute the weight update using:

Δw=−η∂L∂w\Delta w=-\eta\frac{\partial L}{\partial w}

where:

  • η\eta = learning rate

  • LL = loss function

  • ww = model weight

Section 8: Artificial Intelligence

Marks: 10–15
Questions: 8–10

Topics

Search Strategies

  • Uninformed search

  • Informed search

  • Adversarial search

  • BFS

  • DFS

  • Heuristic search

  • Game/search concepts

Logic

  • Propositional logic

  • Predicate logic

  • Logical inference

  • Knowledge representation

Reasoning Under Uncertainty

  • Bayesian networks

  • Conditional independence

  • Bayesian reasoning

Probabilistic Inference

  • Exact inference

  • Variable elimination

  • Approximate inference

  • Sampling-based inference

Question Format

  • MCQ

  • MSQ

  • NAT

Sample Questions

MCQ — BFS

What is the time complexity of BFS in terms of vertices VV and edges EE?

O(V+E)O(V+E)

NAT — Bayesian Update

Given:

P(A)=0.3P(A)=0.3

and

P(B∣A)=0.5P(B|A)=0.5

calculate the required Bayesian probability based on the additional information provided in the question.

Reference Books & Resources

SubjectReference
ProbabilityIntroduction to Probability — Bertsekas & Tsitsiklis
Linear AlgebraIntroduction to Linear Algebra — Gilbert Strang
ProgrammingLearning Python — Mark Lutz
DatabasesDatabase Management Systems — Raghu Ramakrishnan
Machine LearningPattern Recognition and Machine Learning — Christopher Bishop
Artificial IntelligenceArtificial Intelligence: A Modern Approach — Stuart Russell & Peter Norvig

Suggested Preparation Sources

SubjectSuggested Source
GASaurabh Thakur / AptiExpress
Programming & DSAAmit Khurana / Go Classes / Algo / Mindspan Education
SQLVijay Sir
Data WarehousingVishwadeep Gothi Sir
Machine LearningIIT Madras BS Degree Program / Manoj Sir Crash Course
Artificial IntelligencePiyush Viral Sir

Overall Distribution

SectionSubjectQuestionsMarks
1General Aptitude1015
2Probability & Statistics1015–20
3Linear Algebra810–15
4Calculus & Optimization610
5Programming, DS & Algorithms1010–15
6DBMS & Data Warehousing5–75–10
7Machine Learning8–1010–15
8Artificial Intelligence8–1010–15
TotalGA + Core DA/AI65100

High-Priority Core Areas

For preparation efficiency, the major areas to master are:

  1. Probability + Statistics

  2. Linear Algebra

  3. Calculus & Optimization

  4. Python + DSA

  5. SQL + DBMS

  6. Machine Learning

  7. AI + Search + Logic + Bayesian Networks

  8. General Aptitude