| Parameter | Details |
|---|---|
| Duration | 3 Hours |
| Total Marks | 100 |
| Total Questions | 65 |
| General Aptitude (GA) | 10 Questions — 15 Marks |
| Core DA/AI Subjects | 55 Questions — 85 Marks |
| Question Types | MCQ, MSQ, NAT |
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1-mark MCQ: −1/3 for an incorrect answer
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2-mark MCQ: −2/3 for an incorrect answer
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MSQ: As per the question-specific marking scheme
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NAT: No negative marking
Marks: 15
Questions: 10
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Basic arithmetic
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Percentages
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Ratio & proportion
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Analytical reasoning
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Simple probability
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Numerical reasoning
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Verbal reasoning
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Quantitative aptitude
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MCQ
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MSQ
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NAT
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?
Marks: 15–20
Questions: 10
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Counting
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Permutations and combinations
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Probability axioms
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Sample space
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Events
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Conditional probability
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Bayes’ theorem
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Random variables
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Probability distributions
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Mean
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Median
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Mode
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Variance
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Standard deviation
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MCQ
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MSQ
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NAT
MCQ — Permutation
How many ways can 5 distinct books be arranged?
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A. 60
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B. 120
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C. 24
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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)
Marks: 10–15
Questions: 8
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Vectors and vector spaces
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Subspaces
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Linear independence and dependence
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Matrices
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Orthogonal matrices
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Idempotent matrices
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Partition matrices
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Quadratic forms
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Eigenvalues
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Eigenvectors
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Systems of linear equations
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MCQ
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MSQ
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NAT
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
Marks: 10
Questions: 6
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Functions
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Limits
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Continuity
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Differentiability
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Taylor series expansion
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Maxima and minima
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Single-variable optimization
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MCQ
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MSQ
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NAT
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]
Marks: 10–15
Questions: 10
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Python programming
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Python syntax and semantics
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Functions
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Data structures
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Basic problem solving
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Stacks
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Queues
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Linked lists
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Trees
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Hash tables
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Linear search
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Binary search
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Selection sort
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Insertion sort
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Merge sort
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Quick sort
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Graph representation
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BFS
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DFS
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Shortest path
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Basic graph theory
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MCQ
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MSQ
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NAT
MCQ — Quick Sort
What is the worst-case time complexity of Quick Sort?
NAT — Python
Find the output of:
sorted([3, 1, 4, 2])Marks: 5–10
Questions: 5–7
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ER model
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Relational model
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Relational algebra
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Tuple relational calculus
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SQL queries
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Aggregate functions
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Joins
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Subqueries
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Grouping
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Filtering
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Integrity constraints
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Functional dependencies
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Normal forms
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Indexing
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Introduction to data warehousing
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Basic data warehouse concepts
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MCQ
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MSQ
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NAT
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
Marks: 10–15
Questions: 8–10
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Linear regression
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Logistic regression
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Ridge regression
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Support Vector Machines (SVM)
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K-Means clustering
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Clustering techniques
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Dimensionality reduction
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Decision trees
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Naive Bayes
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K-Nearest Neighbors (KNN)
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Basic neural network concepts
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Training
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Weight updates
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Gradient descent
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Accuracy
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Precision
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Recall
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F1-score
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MCQ
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MSQ
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NAT
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:
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η\eta = learning rate
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LL = loss function
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ww = model weight
Marks: 10–15
Questions: 8–10
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Uninformed search
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Informed search
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Adversarial search
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BFS
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DFS
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Heuristic search
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Game/search concepts
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Propositional logic
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Predicate logic
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Logical inference
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Knowledge representation
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Bayesian networks
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Conditional independence
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Bayesian reasoning
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Exact inference
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Variable elimination
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Approximate inference
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Sampling-based inference
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MCQ
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MSQ
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NAT
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.
| Subject | Reference |
|---|---|
| Probability | Introduction to Probability — Bertsekas & Tsitsiklis |
| Linear Algebra | Introduction to Linear Algebra — Gilbert Strang |
| Programming | Learning Python — Mark Lutz |
| Databases | Database Management Systems — Raghu Ramakrishnan |
| Machine Learning | Pattern Recognition and Machine Learning — Christopher Bishop |
| Artificial Intelligence | Artificial Intelligence: A Modern Approach — Stuart Russell & Peter Norvig |
| Subject | Suggested Source |
|---|---|
| GA | Saurabh Thakur / AptiExpress |
| Programming & DSA | Amit Khurana / Go Classes / Algo / Mindspan Education |
| SQL | Vijay Sir |
| Data Warehousing | Vishwadeep Gothi Sir |
| Machine Learning | IIT Madras BS Degree Program / Manoj Sir Crash Course |
| Artificial Intelligence | Piyush Viral Sir |
| Section | Subject | Questions | Marks |
|---|---|---|---|
| 1 | General Aptitude | 10 | 15 |
| 2 | Probability & Statistics | 10 | 15–20 |
| 3 | Linear Algebra | 8 | 10–15 |
| 4 | Calculus & Optimization | 6 | 10 |
| 5 | Programming, DS & Algorithms | 10 | 10–15 |
| 6 | DBMS & Data Warehousing | 5–7 | 5–10 |
| 7 | Machine Learning | 8–10 | 10–15 |
| 8 | Artificial Intelligence | 8–10 | 10–15 |
| Total | GA + Core DA/AI | 65 | 100 |
For preparation efficiency, the major areas to master are:
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Probability + Statistics
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Linear Algebra
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Calculus & Optimization
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Python + DSA
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SQL + DBMS
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Machine Learning
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AI + Search + Logic + Bayesian Networks
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General Aptitude