AI and Coding Question

Unit 1: Introduction to Artificial Intelligence (Coding/Implementation Focus) – 30 MCQs

1. Reflex Agent Implementation

Question:In implementing an intelligent agent for a simple environment (such as a vacuum cleaner world), consider the following Python code for a reflex agent:

def reflex_agent(location, status):  
    if status == 'Dirty':  
        return 'Suck'  
    elif location == 'A':  
        return 'Right'  
    else:  
        return 'Left'  

print(reflex_agent('A', 'Dirty'))

What will be the output of the above code?

Options

a) Right
b) Left
c) Suck
d) None

Correct Answer

✅ c) Suck

Explanation (Detailed)

This code represents a Simple Reflex Agent, which makes decisions only based on the current percept (current location and cleanliness status) and does not use memory or past experience.

Step-by-Step Execution:

1. The function is called as: reflex_agent('A', 'Dirty')
2. The agent first checks: if status == 'Dirty':
   • Since status = 'Dirty', this condition is True.
3. The function immediately returns: 'Suck'
4. The remaining conditions (elif location == 'A' and else) are not executed.

2 Question :-

To demonstrate the PEAS framework (Performance measure, Environment, Actuators, Sensors), consider a Taxi Agent environment.
The following Python function represents the performance measure, where lower distance traveled and lower fuel consumption are preferred.

def performance_measure(distance, fuel):  
    return - (distance + 2 * fuel)

What will be the return value of the function when:

• distance = 5
• fuel = 2

---

Options

a) -9
b) -5
c) -4
d) 9

---

Correct Answer

✅ a) -9

3. Goal-Based Agent Debugging

Question

Debug the following code for a goal-based intelligent agent.
What fix is required to make it return “Goal Reached” when state == goal?

def goal_agent(state, goal):  
    if state = goal:  
        return 'Goal Reached'  
    else:  
        return 'Move'

Options

a) Change = to ==
b) Add else if
c) Remove return
d) None

Correct Answer

✅ a) Change = to ==

Explanation

• = is an assignment operator (invalid in conditions)
• == is a comparison operator
• Goal-based agents compare the current state with the goal

✔ Used in pathfinding and planning algorithms

---

4. Rule-Based AI vs Machine Learning

Question

To show the difference between Rule-Based AI and Machine Learning, consider this rule-based AI code.
What is the output?

def ai_rule(input):  
    if input > 10:  
        return 'High'  
    else:  
        return 'Low'  

print(ai_rule(15))

Options

a) Low
b) High
c) Error
d) None

Correct Answer

✅ b) High

Explanation

• This is Rule-Based AI, not ML
• Decisions are made using fixed if–else rules
• ML would learn thresholds from data

✔ Demonstrates AI ≠ ML

---

5. Narrow AI in Healthcare

Question

To demonstrate Narrow AI in healthcare, consider this simple diagnostic agent.
What is the output for temp = 38?

def health_agent(temp):  
    if temp > 37:  
        return 'Fever'  
    return 'Normal'  

print(health_agent(38))

Options

a) Normal
b) Fever
c) Error
d) None

Correct Answer

✅ b) Fever

Explanation

• Narrow AI performs one specific task
• Uses a fixed threshold
• Common in basic symptom checker apps

✔ Not capable of general medical reasoning

---

6. PEAS Implementation for Vacuum Agent

Question

Implement PEAS logic for a vacuum cleaner agent.
What action will be taken if location = 'B' and dirty = True?

def vacuum_peas(location, dirty):  
    if dirty:  
        return 'Suck'  
    if location == 'A':  
        return 'Right'  
    return 'Left'  

print(vacuum_peas('B', True))

Options

a) Right
b) Left
c) Suck
d) None

Correct Answer

✅ c) Suck

Explanation

• Cleaning (Suck) has highest priority
• PEAS Actuators: Left, Right, Suck
• Reflects vacuum cleaner world

✔ Practical PEAS-based agent simulation

---

7. Debug Rational Agent

Question

Debug the following code to make the rational agent choose the maximum reward action.

def rational_agent(rewards):  
    return min(rewards)

Options

a) Change min to max
b) Add loop
c) Remove return
d) None

Correct Answer

✅ a) Change min to max

Explanation

• Rational agents always select the best (maximum reward) action
• min() selects the worst option

✔ Used in decision-making and utility-based agents

---

8. AI vs Deep Learning (ReLU Example)

Question

To illustrate Deep Learning within AI, consider the ReLU activation function.
What is the output for x = -1?

def relu(x):  
    return max(0, x)  

print(relu(-1))

Options

a) -1
b) 0
c) 1
d) Error

Correct Answer

✅ b) 0

Explanation

• ReLU = max(0, x)
• Removes negative values
• Adds non-linearity in neural networks

✔ Core Deep Learning concept

---

9. Autonomous Vehicle Sensor-Based Agent

Question

For an autonomous vehicle, implement a simple sensor-based agent.
What action is taken when obstacle = True?

def auto_agent(obstacle):  
    if obstacle:  
        return 'Stop'  
    return 'Go'  

print(auto_agent(True))

Options

a) Go
b) Stop
c) Error
d) None

Correct Answer

✅ b) Stop

Explanation

• Reflex agent reacts to sensor input
• Immediate response without planning

✔ Used in obstacle avoidance systems

---

10. PEAS Environment – Sensors

Question

In the following PEAS environment code, what represents the sensor?

class PEAS:  
    def __init__(self):  
        self.sensors = 'Location, Status'  

peas = PEAS()  
print(peas.sensors)

Options

a) Location, Status
b) Action
c) Performance
d) None

Correct Answer

✅ a) Location, Status

Explanation

• Sensors collect environment information
• Essential for agent perception

✔ Core concept in agent design

11. Debug Model-Based Agent (Strong AI Aspiration)

Question

Debug the following code for a model-based intelligent agent.
Which fix is required to correctly update the internal belief?

def model_agent(belief, input):  
    belief = input  
    return belief

Options

a) No fix needed
b) Add if condition
c) Change = to ==
d) Add return input

Correct Answer

✅ a) No fix needed

Explanation

• Model-based agents update their internal belief state
• Assignment (=) is correct here
• The updated belief is returned properly

✔ Practical for advanced agent design and Strong AI concepts

---

12. Machine Learning as a Subset of AI

Question

To show ML as a subset of AI, consider a simple linear ML model.
What is the prediction for x = 2, slope = 3, intercept = 1?

def ml_model(x, slope, intercept):  
    return slope * x + intercept  

print(ml_model(2, 3, 1))

Options

a) 6
b) 7
c) 5
d) None

Correct Answer

✅ b) 7

Explanation

$$3 \times 2 + 1 = 7$$3×2+1=7

✔ Demonstrates prediction using learned parameters

---

13. ChatGPT-Like Rule-Based Agent

Question

For a ChatGPT-like application, implement a simple rule-based chat agent.
What is the response for input "hello"?

def chat_agent(input):  
    if input == 'hello':  
        return 'Hi'  
    return 'Unknown'  

print(chat_agent('hello'))

Options

a) Unknown
b) Hi
c) Error
d) None

Correct Answer

✅ b) Hi

Explanation

• Narrow AI
• Fixed rule-based response
• No learning involved

---

14. Agent–Environment Interaction

Question

Implement an environment for an agent.
What is the next state after action 'Right' from state 'A'?

def environment(state, action):  
    if action == 'Right' and state == 'A':  
        return 'B'  
    return state  

print(environment('A', 'Right'))

Correct Answer

✅ b) B

Explanation

✔ Demonstrates state transition in agent-environment interaction

---

15. Debug PEAS Actuators

Question

Debug the PEAS actuator code.
Which fix ensures correct action output?

def peas_actuators(performance):  
    if performance < 0:  
        return 'Improve'

Correct Answer

✅ a) Add else return ‘Good’

Explanation

• PEAS loop must handle all performance cases
• Ensures proper feedback

---

16. Deep Learning Layer Example

Question

To differentiate Deep Learning from AI, compute the output of this neural layer.

def dl_layer(inputs, weights):  
    return sum(i*w for i,w in zip(inputs, weights))  

print(dl_layer([1,2], [3,4]))

Correct Answer

✅ a) 11

Explanation

$$1×3 + 2×4 = 11$$1×3+2×4=11

✔ Weighted sum = basic DL operation

---

17. Healthcare Alert Agent

Question

For a healthcare application, what is the action if heart_rate = 110?

def health_alert(heart_rate):  
    if heart_rate > 100:  
        return 'Alert'  
    return 'Normal'  

print(health_alert(110))

Correct Answer

✅ b) Alert

Explanation

✔ Threshold-based medical alert system

---

18. Utility-Based Agent

Question

What does the utility-based agent return?

def utility_agent(options):  
    return max(options)  

print(utility_agent([5,10]))

Correct Answer

✅ b) 10

Explanation

✔ Utility-based agents select maximum utility

---

19. Debug AI vs ML Fit Function

Question

Is any fix required in this ML fit function?

def ml_fit(data):  
    return sum(data) / len(data)

Correct Answer

✅ a) No fix

Explanation

✔ Mean calculation represents a simple ML model

---

20. Autonomous Sensor Agent

Question

For autonomous systems, what action is taken if distance = 5?

def auto_sensor(distance):  
    if distance < 10:  
        return 'Brake'  
    return 'Accelerate'  

print(auto_sensor(5))

Correct Answer

✅ b) Brake

Explanation

✔ Reflex-based safety behavior

---

21. PEAS Performance Measure

Question

Compute the PEAS performance score.

def peas_performance(actions, goals):  
    return goals / actions  

print(peas_performance(3, 2))

Correct Answer

✅ a) 0.666…

Explanation

✔ Performance metric in PEAS framework

---

22. Adaptive Agent (General AI Aspiration)

Question

What is the updated value?

def adaptive_agent(value, error):  
    if error:  
        value += 1  
    return value  

print(adaptive_agent(5, True))

Correct Answer

✅ b) 6

Explanation

✔ Adaptive agent updates internal state

---

23. ChatGPT Simulation (Multi-Response Agent)

Question

What is the response for input 'bye'?

def chat_multi(input):  
    responses = {'hello': 'Hi', 'bye': 'Goodbye'}  
    return responses.get(input, 'Unknown')  

print(chat_multi('bye'))

Correct Answer

✅ b) Goodbye

---

24. Debug Environment Update Code

Question

Is a fix required?

def env_update(state, action):  
    if action == 'Move':  
        state = 'New'  
    return state

Correct Answer

✅ a) No fix

Explanation

✔ Environment updates state correctly

---

25. Distinguish AI Agent from ML

Question

What is the output?

def ai_agent(input):  
    if input == 'danger':  
        return 'Avoid'  

print(ai_agent('danger'))

Correct Answer

✅ a) Avoid

(Note: Without else, other inputs return None)

---

26. Simple DL Activation (Sigmoid)

Question

What is the sigmoid output for x = 0?

import math  

def sigmoid(x):  
    return 1 / (1 + math.exp(-x))  

print(sigmoid(0))

Correct Answer

✅ b) 0.5

Explanation

✔ Standard sigmoid function

---

27. Healthcare Monitoring Agent

Question

What is the output for value = 40?

def monitor_agent(value):  
    if value < 50:  
        return 'Low'  
    return 'OK'  

print(monitor_agent(40))

Correct Answer

✅ b) Low

---

28. PEAS Environment – Actuators

Question

What are the actuators?

class PEAS:  
    actuators = ['Move', 'Act']  

print(PEAS.actuators)

Correct Answer

✅ a) [‘Move’, ‘Act’]

---

29. Debug Goal Agent Code

Question

Is any fix required?

def goal_check(current, goal):  
    return current == goal

Correct Answer

✅ a) No fix

Explanation

✔ Correct equality check

---

30. Autonomous Decision Agent

Question

What is the action if speed = 70?

def speed_agent(speed):  
    if speed > 60:  
        return 'Slow Down'  
    return 'Maintain'  

print(speed_agent(70))

Correct Answer

✅ b) Slow Down

Explanation

✔ Threshold-based autonomous decision making

Unit 2: Mathematics for Artificial Intelligence

Linear Algebra (Questions 1–16)

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1. Vector Addition Using NumPy

Question

Implement vector addition in Python using NumPy. What is the output?

import numpy as np  

v1 = np.array([1, 2])  
v2 = np.array([3, 4])  

print(v1 + v2)

Options

a) [4 6]
b) [1 2 3 4]
c) Error
d) 10

✅ Correct Answer

a) [4 6]

Explanation

• Vector addition is performed element-wise
• Used in feature vector combination in AI/ML

---

2. Debug Matrix Multiplication

Question

Fix the code to perform matrix multiplication correctly.

import numpy as np  

A = np.array([[1, 2], [3, 4]])  
B = np.array([[5, 6], [7, 8]])  

print(A * B)  # Element-wise, wrong for matmul

Options

a) Change * to @
b) Add np.dot(A, B)
c) Both a and b
d) None

✅ Correct Answer

c) Both a and b

Explanation

• * → element-wise multiplication
• @ or np.dot() → matrix multiplication
• Used in neural network layers

---

3. Eigenvalue Computation

Question

Find eigenvalues of the matrix [[1,0],[0,2]].

import numpy as np  

M = np.array([[1,0],[0,2]])  
eigvals = np.linalg.eigvals(M)  

print(eigvals)

Options

a) [1. 2.]
b) [0. 0.]
c) Error
d) [3.]

✅ Correct Answer

a) [1. 2.]

Explanation

• Eigenvalues represent scaling along axes
• Used in PCA and covariance analysis

---

4. Eigenvector Finding

Question

What is the shape of eigenvectors for a 2×2 matrix?

import numpy as np  

M = np.array([[1,2],[3,4]])  
_, eigvecs = np.linalg.eig(M)  

print(eigvecs.shape)

Options

a) (2,2)
b) (2,)
c) Error
d) (4,)

✅ Correct Answer

a) (2,2)

Explanation

• Each column is an eigenvector
• Used in dimensionality reduction

---

5. Debug SVD Code

Question

Is any fix required in this SVD code?

import numpy as np  

M = np.array([[1,2],[3,4]])  
U, S, V = np.linalg.svd(M, full_matrices=False)  

print(S.shape)

✅ Correct Answer

a) No fix

Explanation

• Correct SVD implementation
• Used in PCA, compression, noise reduction

---

6. Matrix Inverse (Singular Matrix)

Question

What happens if the matrix is singular?

import numpy as np  

M = np.array([[1,2],[2,4]])  
try:  
    inv = np.linalg.inv(M)  
except:  
    print('Singular')

✅ Correct Answer

a) Prints Singular

Explanation

• Singular matrices are non-invertible
• Important for linear regression stability

---

7. Vector Dot Product

Question

Compute dot product of [1,2] and [3,4].

import numpy as np  

v1 = np.array([1,2])  
v2 = np.array([3,4])  

print(np.dot(v1, v2))

✅ Correct Answer

a) 11

Explanation

$$1×3 + 2×4 = 11$$1×3+2×4=11

✔ Used in cosine similarity

---

8. Matrix Transpose

Question

What is the output shape after transpose?

import numpy as np  

M = np.array([[1,2],[3,4],[5,6]])  
print(M.T.shape)

✅ Correct Answer

a) (2,3)

Explanation

• Transpose swaps rows and columns
• Used in data preprocessing

---

9. Eigenvalues with Complex Numbers

Question

Does NumPy handle complex eigenvalues?

import numpy as np  

M = np.array([[0,-1],[1,0]])  
eig = np.linalg.eig(M)  

print(eig[0])

✅ Correct Answer

a) No fix, handles complex

Explanation

• NumPy supports complex eigenvalues
• Used in rotations & transformations

---

10. Vector Norm

Question

Find the norm of [3,4].

import numpy as np  

v = np.array([3,4])  
print(np.linalg.norm(v))

✅ Correct Answer

a) 5

Explanation

$$\sqrt{3^2 + 4^2} = 5$$32+42​=5

✔ Used in distance metrics

---

11. SVD Low-Rank Approximation

Question

What is the output shape of low-rank approximation?

import numpy as np  

M = np.array([[1,2],[3,4]])  
U, S, V = np.linalg.svd(M)  
approx = U[:,:1] @ np.diag(S[:1]) @ V[:1,:]  

print(approx.shape)

✅ Correct Answer

a) (2,2)

Explanation

• Rank-1 approximation
• Used in compression & recommender systems

---

12. Vector Norm (L2 Norm)

Question

Compute L2 norm of [3,4].

import numpy as np  

v = np.array([3,4])  
print(np.linalg.norm(v))

✅ Correct Answer

a) 5.0

---

13. Matrix Determinant

Question

Find determinant of [[1,2],[3,4]].

import numpy as np  

M = np.array([[1,2],[3,4]])  
print(np.linalg.det(M))

✅ Correct Answer

a) -2.0

Explanation

$$(1×4) – (2×3) = -2$$(1×4)−(2×3)=−2

✔ Used to check invertibility

---

14. Debug Eigenvector Code

Question

Fix code to obtain eigenvalues and eigenvectors.

import numpy as np  

M = np.array([[1,0],[0,1]])  
eigvals = np.linalg.eigvals(M)

✅ Correct Answer

a) Change to np.linalg.eig

---

15. Matrix Rank

Question

Find the rank of [[1,2],[2,4]].

import numpy as np  

M = np.array([[1,2],[2,4]])  
print(np.linalg.matrix_rank(M))

✅ Correct Answer

a) 1

Explanation

• Rows are linearly dependent
• Used in SVD & feature reduction

---

16. Vector Projection

Question

Find projection of [1,0] onto [1,1].

import numpy as np  

u = np.array([1,0])  
v = np.array([1,1])  
proj = np.dot(u, v) / np.dot(v, v) * v  

print(proj)

✅ Correct Answer

a) [0.5 0.5]

Unit 2: Mathematics for Artificial Intelligence

Probability & Statistics (Questions 16–25)

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16. Random Variable Sampling from Normal Distribution

Question

Generate random samples from a normal distribution. What is the mean of 100 samples?

import numpy as np  

samples = np.random.normal(0, 1, 100)  
print(np.mean(samples))

Options

a) Close to 0
b) Exactly 1
c) Error
d) None

✅ Correct Answer

a) Close to 0

Explanation

• Samples are drawn from N(0, 1)
• By the Law of Large Numbers, the sample mean approaches 0
• Used in Monte Carlo simulations

---

17. Bayes Theorem

Question

Compute $P(A|B)$P(A∣B) using Bayes theorem.

def bayes(p_b_a, p_a, p_b):  
    return (p_b_a * p_a) / p_b  

print(bayes(0.8, 0.3, 0.4))

Options

a) 0.6
b) 0.32
c) Error
d) 0.24

✅ Correct Answer

a) 0.6

18. Poisson Distribution – PMF

Question

Find $P(k=2, \lambda=3)$P(k=2,λ=3) using Poisson PMF.

from scipy.stats import poisson  

print(poisson.pmf(2, 3))

Options

a) ~0.224
b) 1
c) Error
d) 0

✅ Correct Answer

a) ~0.224

Explanation

• Poisson models event counts
• Used in queueing systems & traffic modeling

---

19. Hypothesis Test (t-test)

Question

Is any fix required in this independent t-test code?

from scipy import stats  

data1 = [1,2,3]  
data2 = [4,5,6]  
t, p = stats.ttest_ind(data1, data2)  
print(p)

Options

a) No fix
b) Use Mann–Whitney U
c) Add equal_var
d) None

✅ Correct Answer

a) No fix

Explanation

• Correct two-sample t-test
• Used in A/B testing

---

20. Normal Distribution – CDF

Question

Compute CDF of standard normal at 0.

from scipy.stats import norm  

print(norm.cdf(0))

Options

a) 0.5
b) 0
c) 1
d) Error

✅ Correct Answer

a) 0.5

Explanation

• Symmetry of standard normal distribution
• Widely used in probability estimation

---

21. Random Variable Variance

Question

Find variance of [1,2,3].

import numpy as np  

data = np.array([1,2,3])  
print(np.var(data))

Options

a) 0.666…
b) 2
c) Error
d) 1

✅ Correct Answer

a) 0.666…

✔ Measures spread of data

---

22. Bayes Update (Posterior)

Question

Compute posterior probability using Bayes update.

def bayes_update(prior, lik, ev):  
    return (lik * prior) / ev  

print(bayes_update(0.5, 0.9, 0.6))

Options

a) 0.75
b) 0.45
c) Error
d) 0.3

✅ Correct Answer

a) 0.75

23. Poisson Distribution – CDF

Question

Compute Poisson CDF for $k = 2, \lambda = 1$k=2,λ=1.

from scipy.stats import poisson  

print(poisson.cdf(2, 1))

Options

a) ~0.919
b) 1
c) Error
d) 0

✅ Correct Answer

a) ~0.919

Explanation

• CDF gives probability of ≤ k events
• Used in queueing theory

---

24. Hypothesis P-Value (Chi-Square)

Question

Compute p-value for chi-square test.

from scipy.stats import chi2  

print(1 - chi2.cdf(5, 2))

Options

a) ~0.082
b) 1
c) Error
d) 0

✅ Correct Answer

a) ~0.082

Explanation

• P-value measures statistical significance
• Used in feature independence tests

---

25. Uniform Random Variable

Question

Is any fix required in this uniform random sampling code?

import numpy as np  

samples = np.random.uniform(0, 1, 10)  
print(np.mean(samples))

Options

a) No fix
b) Change to normal
c) Add seed
d) None

✅ Correct Answer

a) No fix

Explanation

• Correct uniform sampling
• Mean ≈ 0.5 for large samples
• Used in random initialization

Jharkhand JSSC JTMACCE-2025

📘 Calculus (Questions 26–30)

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26. Derivative Approximation (Finite Difference)

Question

Approximate the derivative of $f(x) = x^2$f(x)=x2 at $x = 2$x=2 using finite differences with $h = 0.001$h=0.001.

def deriv(f, x, h):  
    return (f(x+h) - f(x)) / h  

f = lambda x: x**2  
print(deriv(f, 2, 0.001))

Options

a) ~4.001
b) 2
c) Error
d) 0

✅ Answer

a) ~4.001

Explanation

• True derivative of $x^2$x2 is $2x$2x
• At $x=2$x=2: derivative = 4
• Finite difference gives a close approximation
• Widely used in numerical optimization and gradient estimation

---

27. Partial Derivative (w.r.t x)

Question

Compute the partial derivative with respect to x of
$f(x, y) = x^2 + y^2$f(x,y)=x2+y2 at $(1,1)$(1,1).

def partial_x(x, y, h):  
    f = lambda x: x**2 + y**2  
    return (f(x+h) - f(x)) / h  

print(partial_x(1, 1, 0.001))

Options

a) ~2.001
b) 1
c) Error
d) 0

✅ Answer

a) ~2.001

28. Gradient Vector

Question

Find the gradient of vector function

at point (1,1).

import numpy as np  

def grad(x, y):  
    return np.array([2*x, 3*y**2])  

print(grad(1,1))

Options

a) [2 3]
b) [1 1]
c) Error
d) [0 0]

✅ Answer

a) [2 3]

Explanation

• Gradient gives direction of steepest increase
• Essential for gradient descent algorithms

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29. Gradient Descent Update Step

Question

Perform one GD step for loss $L = w^2$L=w2 with learning rate = 0.1 and $w = 2$w=2.

def gd_step(w, lr):  
    grad = 2*w  
    return w - lr * grad  

print(gd_step(2, 0.1))

Options

a) 1.6
b) 2.4
c) Error
d) 0

✅ Answer

a) 1.6

Explanation

• Gradient = $2w = 4$2w=4
• Update: $2 – 0.1×4 = 1.6$2−0.1×4=1.6
• Core concept in machine learning training

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30. Debug Gradient Descent Loop

Question

Is the following gradient descent loop correct?

w = 10  
for _ in range(10):  
    w -= 0.1 * 2*w  
print(w)

Options

a) No fix, converges to 0
b) Add break
c) Change -= to +=
d) None

✅ Answer

a) No fix, converges to 0

Explanation

• Correct GD formula applied
• Minimizes $w^2$w2
• Converges toward global minimum $w=0$w=0

---

📘 Calculus (Questions 31–35)

---

31. Partial Derivative (w.r.t y)

Question

Find $\frac{\partial}{\partial y}(x·y)$∂y∂​(x⋅y) at $x=2, y=3$x=2,y=3.

def partial_y(x, y, h):  
    f = lambda y: x * y  
    return (f(y+h) - f(y)) / h  

print(partial_y(2, 3, 0.001))

Options

a) ~2.0
b) 3.0
c) Error
d) 6.0

✅ Answer

a) ~2.0

Explanation

• $\frac{\partial}{\partial y}(xy) = x$∂y∂​(xy)=x
• Used in loss function gradients

---

32. Stochastic Gradient Descent (SGD)

Question

Perform one SGD update step.

def sgd(w, sample_grad, lr):  
    return w - lr * sample_grad  

print(sgd(1, 0.5, 0.1))

Options

a) 0.95
b) 1.05
c) Error
d) 0

✅ Answer

a) 0.95

Explanation

• Single-sample update
• Used for large-scale ML problems

---

33. Optimization Convergence Check

Question

Check if optimization has converged.

def converge(old, new, tol=0.01):  
    return abs(old - new) < tol  

print(converge(1.0, 1.005))

Options

a) True
b) False
c) Error
d) None

✅ Answer

a) True

Explanation

• Difference < tolerance → convergence achieved
• Used in iterative algorithms

---

34. Debug Vector Gradient

Question

Is the vector gradient implementation correct?

import numpy as np  

def vec_grad(params):  
    return np.array([2*params[0], 2*params[1]])  

print(vec_grad([1,2]))

Options

a) No fix
b) Change to [params**2]
c) Add np.gradient
d) None

✅ Answer

a) No fix

Explanation

• Correct gradient of $x^2 + y^2$x2+y2
• Common in multi-parameter optimization

---

35. Adam Optimizer Step (Simplified)

Question

Perform a simplified Adam optimizer step.

def adam_step(w, grad, lr=0.01):  
    return w - lr * grad  

print(adam_step(5, 2))

Options

a) 4.98
b) 5.02
c) Error
d) None

✅ Answer

a) 4.98

Explanation

• Simplified form (without momentum terms)
• Adam is widely used in deep learning

📘 Discrete Mathematics (Questions 36–40)

---

36. Graph Adjacency List

Question

Implement a graph using an adjacency list.
What are the neighbors of node 0?

graph = {0: [1,2], 1: [0], 2: [0]}  
print(graph[0])

Options

a) [1,2]
b) [0]
c) Error
d) None

✅ Answer

a) [1,2]

Explanation

• Adjacency list stores neighbors of each vertex
• Node 0 is connected to nodes 1 and 2
• Widely used in graph and network algorithms

---

37. Breadth-First Search (BFS) Traversal

Question

Implement BFS traversal for a tree.
What is the visit order starting from root 0?

from collections import deque  

def bfs(root, graph):  
    q = deque([root])  
    visited = []  
    while q:  
        node = q.popleft()  
        visited.append(node)  
        q.extend(graph[node])  
    return visited  

graph = {0: [1,2], 1: [], 2: []}  
print(bfs(0, graph))

Options

a) [0,1,2]
b) [0,2,1]
c) Error
d) [1,2,0]

✅ Answer

a) [0,1,2]

Explanation

• BFS explores level by level
• Uses a queue (FIFO)
• Practical in search problems, shortest path in unweighted graphs

---

38. Graph Cycle Detection

Question

Debug the following code for cycle detection in an undirected graph.

def has_cycle(graph):  
    # Simple check for self-loop  
    for node in graph:  
        if node in graph[node]:  
            return True  
    return False

Options

a) No fix for simple
b) Add DFS
c) Change to directed
d) None

✅ Answer

b) Add DFS

Explanation

• Checking only self-loops is insufficient
• Cycles in undirected graphs require:
  • DFS with visited + parent tracking
• Essential in network reliability and graph validation

---

39. Network Shortest Path (Dijkstra’s Algorithm)

Question

Compute the shortest distance from node 0 to node 1.

import heapq  

def short_path(graph, start):  
    dist = {n: float('inf') for n in graph}  
    dist[start] = 0  
    pq = [(0, start)]  
    while pq:  
        d, node = heapq.heappop(pq)  
        for nei, w in graph[node]:  
            new_d = d + w  
            if new_d < dist[nei]:  
                dist[nei] = new_d  
                heapq.heappush(pq, (new_d, nei))  
    return dist  

graph = {0: [(1,5)], 1: []}  
print(short_path(graph, 0)[1])

Options

a) 5
b) inf
c) Error
d) 0

✅ Answer

a) 5

Explanation

• Dijkstra’s algorithm computes minimum path cost
• Edge weight from 0 → 1 is 5
• Used in routing, GPS, network optimization

---

40. Tree Height Calculation

Question

Compute the height of a tree.

def tree_height(tree, node):  
    if not tree[node]:  
        return 0  
    return 1 + max(tree_height(tree, child) for child in tree[node])  

tree = {0: [1,2], 1: [], 2: []}  
print(tree_height(tree, 0))

Options

a) 1
b) 2
c) 0
d) Error

✅ Answer

a) 1

Explanation

• Tree height = longest path from root to leaf
• Root → leaf = 1 edge
• Important in decision trees, recursion depth, balancing

Jharkhand JSSC JTMACCE-2025

📘 Unit 3: Programming Fundamentals & Coding

Programming Languages Basics (Questions 1–15)

---

1. Data Types in Python

Question

What is the output of the following Python code?

x = 5  
y = "Hello"  
print(type(x), type(y))

Options

a) <class 'int'> <class 'str'>
b) int str
c) Error
d) 5 Hello

✅ Answer

a) <class 'int'> <class 'str'>

Explanation

• type() returns the data type of a variable
• x is an integer, y is a string
• Practical for debugging and type checking

---

2. Debug C++ Conditional Statement

Question

Fix the C++ code to print “Pass” if score > 50.

#include <iostream>  
int main() {  
    int score = 60;  
    if (score > 50)  
        std::cout << "Pass";  
    else  
        std::cout << "Fail";  
    return 0;  
}

Options

a) No fix needed
b) Add {} around cout
c) Change > to <
d) None

✅ Answer

a) No fix needed

Explanation

• Condition is correct
• Code prints “Pass”
• Practical in grading and decision logic

---

3. Python Loop to Sum 1 to 5

Question

What is the output?

sum = 0  
for i in range(1,6):  
    sum += i  
print(sum)

Options

a) 15
b) 6
c) Error
d) 5

✅ Answer

a) 15

Explanation

• Adds numbers from 1 to 5
• Formula: 1+2+3+4+5 = 15
• Practical in accumulation problems

---

4. Python Function to Return Square

Question

Debug the function.

def square(x):  
    return x * x  

print(square(3))

Options

a) No fix
b) Change * to **
c) Add int(x)
d) None

✅ Answer

a) No fix

Explanation

• x * x correctly computes square
• Practical in mathematical functions

---

5. C++ Loop Output

Question

What is the output?

#include <iostream>  
int main() {  
    for(int i=1; i<=3; i++) {  
        std::cout << i << " ";  
    }  
    return 0;  
}

Options

a) 1 2 3
b) 1 2
c) Error
d) Infinite

✅ Answer

a) 1 2 3

Explanation

• Loop runs from 1 to 3
• Practical in iteration and counters

---

6. Python Module Import

Question

What is the output if mymod.py contains def greet(): return "Hi"?

import mymod  
print(mymod.greet())

Options

a) Hi
b) Error
c) greet
d) None

✅ Answer

a) Hi

Explanation

• Functions are accessed via module.function()
• Practical in code reuse and modular programming

---

7. Debug C++ Data Type

Question

Fix the C++ code to print a float.

#include <iostream>  
int main() {  
    float f = 3.14;  
    std::cout << f;  
    return 0;  
}

Options

a) No fix
b) Change float to int
c) Add cast
d) None

✅ Answer

a) No fix

Explanation

• Float is correctly declared and printed
• Practical in numeric computations

---

8. Python Conditional Statement

Question

Output if age = 20?

age = 20  
if age >= 18:  
    print("Adult")  
else:  
    print("Minor")

Options

a) Adult
b) Minor
c) Error
d) None

✅ Answer

a) Adult

Explanation

• Age ≥ 18 → Adult
• Practical in eligibility checks

---

9. Implement C++ Function

Question

What is the output?

#include <iostream>  
int add(int a, int b) {  
    return a + b;  
}  
int main() {  
    std::cout << add(2,3);  
    return 0;  
}

Options

a) 5
b) Error
c) 23
d) None

✅ Answer

a) 5

Explanation

• Function returns sum
• Practical in modular programming

---

10. Debug Python Loop

Question

Fix the loop to print 0 to 4.

i = 0  
while i < 5:  
    print(i)  
    i += 1

Options

a) No fix
b) Change < to >
c) Add break
d) None

✅ Answer

a) No fix

Explanation

• Loop correctly prints 0–4
• Practical in while-loop logic

---

11. Python Data Type Conversion

Question

What is the output?

print(int("123"))

Options

a) 123
b) “123”
c) Error
d) None

✅ Answer

a) 123

Explanation

• Converts string to integer
• Practical in user input handling

---

12. C++ Conditional Statement

Question

Output if x=5, y=10?

#include <iostream>  
int main() {  
    int x=5, y=10;  
    if(x < y) std::cout << "Less";  
    else std::cout << "More";  
    return 0;  
}

Options

a) Less
b) More
c) Error
d) None

✅ Answer

a) Less

Explanation

• 5 < 10 → “Less”
• Practical in comparisons

---

13. Python Loop with Break

Question

What is the output?

for i in range(5):  
    if i == 3:  
        break  
    print(i)

Options

a) 0 1 2
b) 0 1 2 3 4
c) Error
d) 3

✅ Answer

a) 0 1 2

Explanation

• break exits loop at i = 3
• Practical in early termination

---

14. Debug C++ Include Statement

Question

Fix the code to use sqrt().

#include <iostream>  
int main() {  
    std::cout << sqrt(4);  
    return 0;  
}

Options

a) Add #include <cmath>
b) Change sqrt to pow
c) Remove main
d) None

✅ Answer

a) Add #include <cmath>

Explanation

• Math functions need <cmath>
• Practical in library usage

---

15. Python Function with Default Argument

Question

What is the output?

def func(x=10):  
    return x * 2  

print(func(5))

Options

a) 10
b) 20
c) Error
d) None

✅ Answer

a) 10

Explanation

• Passed value 5 overrides default
• Default used only when no argument provided
• Practical in flexible APIs

📘 Unit 3: Programming Fundamentals & Coding

Programming + Object-Oriented Programming (Questions 16–25)

---

16. C++ Loop with continue

Question

What is the output of the following C++ code?

#include <iostream>  
int main() {  
    for(int i=1; i<=3; i++) {  
        if(i==2) continue;  
        std::cout << i << " ";  
    }  
    return 0;  
}

Options

a) 1 3
b) 1 2 3
c) Error
d) 2

✅ Answer

a) 1 3

Explanation

• continue skips the current iteration
• When i == 2, printing is skipped
• Output includes only 1 and 3
• Practical in selective processing

---

17. Python List Concatenation

Question

What is the output?

print([1,2] + [3])

Options

a) [1,2,3]
b) 6
c) Error
d) None

✅ Answer

a) [1,2,3]

Explanation

• + concatenates lists
• Does not perform arithmetic addition
• Practical in data aggregation

---

18. Debug Python Module Alias

Question

Fix the code to print the value of π.

import math as m  
print(m.pi)

Options

a) No fix
b) Change to import math
c) Add from math import pi
d) None

✅ Answer

a) No fix

Explanation

• Alias m correctly refers to math
• m.pi is valid
• Practical in clean imports

---

19. C++ Nested Conditional

Question

What is the output if a=5, b=10, c=15?

#include <iostream>  
int main() {  
    int a=5, b=10, c=15;  
    if(a < b) {  
        if(b < c) std::cout << "Increasing";  
    }  
    return 0;  
}

Options

a) Increasing
b) Nothing
c) Error
d) None

✅ Answer

a) Increasing

Explanation

• Both conditions are true
• Nested if executes
• Practical in multi-level decision logic

---

20. Python Module Function Call

Question

If mymod.py contains:

def greet():
    return "Hi"

What is the output?

import mymod  
print(mymod.greet())

Options

a) Hi
b) Error
c) greet
d) None

✅ Answer

a) Hi

Explanation

• Module functions are accessed via module.function()
• Practical in code reuse and modularity

---

🧩 Object-Oriented Programming (Questions 21–25)

---

21. Python Class Implementation

Question

What is the output?

class A:  
    def __init__(self):  
        self.x = 5  

obj = A()  
print(obj.x)

Options

a) 5
b) Error
c) None
d) A

✅ Answer

a) 5

Explanation

• Constructor initializes instance variable x
• Practical in data encapsulation

---

22. Debug C++ Class Code

Question

Fix the code to print 10.

#include <iostream>  
class B {  
    public:  
        int y = 10;  
};  

int main() {  
    B obj;  
    std::cout << obj.y;  
    return 0;  
}

Options

a) No fix
b) Add constructor
c) Change public to private
d) None

✅ Answer

a) No fix

Explanation

• Public members are directly accessible
• Practical in basic OOP design

---

23. Python Inheritance

Question

What is the output?

class Parent:  
    def method(self):  
        return "Parent"  

class Child(Parent):  
    pass  

c = Child()  
print(c.method())

Options

a) Parent
b) Child
c) Error
d) None

✅ Answer

a) Parent

Explanation

• Child inherits methods from Parent
• Practical in code reuse

---

24. C++ Polymorphism

Question

What is the output?

#include <iostream>  
class Base {  
    public:  
        virtual void show() { std::cout << "Base"; }  
};  

class Derived : public Base {  
    public:  
        void show() { std::cout << "Derived"; }  
};  

int main() {  
    Base* b = new Derived();  
    b->show();  
    return 0;  
}

Options

a) Derived
b) Base
c) Error
d) None

✅ Answer

a) Derived

Explanation

• virtual enables runtime polymorphism
• Function resolved at runtime
• Practical in dynamic binding

---

25. Python Encapsulation (Private Members)

Question

Fix the code to access private variable __z.

class C:  
    def __init__(self):  
        self.__z = 20  

obj = C()  
print(obj.__z)

Options

a) Change to obj._C__z
b) Make variable public
c) Add getter method
d) All

✅ Answer

d) All

Explanation

• Python uses name mangling
• Access possible via mangled name, getter, or public variable
• Practical in data hiding

Unit 3: Programming Fundamentals & Coding

Object-Oriented Programming (Questions 26–40)

Formatted consistently with clear questions, options, correct answers, and practical explanations.

---

📘 Unit 3: Programming Fundamentals & Coding

Object-Oriented Programming (Questions 26–40)

---

26. Python Class Method

Question

What is the output of A.greet()?

class A:  
    @classmethod  
    def greet(cls):  
        return "Hi"  

print(A.greet())

Options

a) Hi
b) Error
c) A
d) None

✅ Answer

a) Hi

Explanation

• @classmethod works on the class, not instance
• cls refers to class A
• Practical in factory methods and shared behavior

---

27. C++ Inheritance

Question

What is the output?

#include <iostream>  
class Base {  
    public:  
        int val = 100;  
};  

class Derived : public Base {};  

int main() {  
    Derived d;  
    std::cout << d.val;  
    return 0;  
}

Options

a) 100
b) Error
c) 0
d) None

✅ Answer

a) 100

Explanation

• Public members are inherited
• Practical in class reuse

---

28. Python Polymorphism (Method Overriding)

Question

What is the output?

class Animal:  
    def sound(self):  
        return "Sound"  

class Dog(Animal):  
    def sound(self):  
        return "Bark"  

d = Dog()  
print(d.sound())

Options

a) Bark
b) Sound
c) Error
d) None

✅ Answer

a) Bark

Explanation

• Child overrides parent method
• Demonstrates runtime polymorphism

---

29. Debug C++ Constructor Code

Question

Fix the constructor call.

#include <iostream>  
class D {  
    public:  
        D() { std::cout << "Created"; }  
};  

int main() {  
    D obj;  
    return 0;  
}

Options

a) No fix
b) Add parameters
c) Remove ()
d) None

✅ Answer

a) No fix

Explanation

• Constructor automatically executes on object creation
• Practical in object initialization

---

30. Python Encapsulation with Getter

Question

What is the output?

class E:  
    def __init__(self):  
        self.__secret = "Hidden"  

    def get_secret(self):  
        return self.__secret  

e = E()  
print(e.get_secret())

Options

a) Hidden
b) Error
c) __secret
d) None

✅ Answer

a) Hidden

Explanation

• Getter provides controlled access
• Demonstrates encapsulation

---

31. C++ Polymorphism with Virtual Function

Question

What is the output?

#include <iostream>  
class Base {  
    public:  
        virtual ~Base() {}  
        virtual void print() { std::cout << "Base"; }  
};  

class Derived : public Base {  
    void print() { std::cout << "Derived"; }  
};  

int main() {  
    Base* b = new Derived();  
    b->print();  
    delete b;  
    return 0;  
}

Options

a) Derived
b) Base
c) Error
d) None

✅ Answer

a) Derived

Explanation

• Virtual functions enable dynamic binding
• Method resolved at runtime

---

32. Python Multiple Inheritance (MRO)

Question

What is the output?

class A:  
    def method(self):  
        return "A"  

class B:  
    def method(self):  
        return "B"  

class C(A, B):  
    pass  

c = C()  
print(c.method())

Options

a) A
b) B
c) Error
d) None

✅ Answer

a) A

Explanation

• Python follows Method Resolution Order (left-to-right)
• A is searched before B

---

33. Python Class Attribute

Question

Fix to print 42.

class F:  
    attr = 42  

print(F.attr)

Options

a) No fix
b) Add self
c) Create instance
d) None

✅ Answer

a) No fix

Explanation

• Class attributes accessed via class name
• Practical in shared constants

---

34. C++ Encapsulation with Private Member

Question

What is the output?

#include <iostream>  
class G {  
    private:  
        int priv = 50;  
    public:  
        int get_priv() { return priv; }  
};  

int main() {  
    G g;  
    std::cout << g.get_priv();  
    return 0;  
}

Options

a) 50
b) Error (private)
c) None
d) 0

✅ Answer

a) 50

Explanation

• Private data accessed via public method
• Core concept of encapsulation

---

35. Python super() in Inheritance

Question

What is the output?

class Parent:  
    def method(self):  
        return "Parent"  

class Child(Parent):  
    def method(self):  
        return super().method() + " Child"  

c = Child()  
print(c.method())

Options

a) Parent Child
b) Child
c) Error
d) None

✅ Answer

a) Parent Child

Explanation

• super() calls parent implementation
• Used to extend behavior

---

37. C++ Friend Function

Question

What does the friend function allow?

#include <iostream>  
class H {  
    private:  
        int data = 60;  
    friend void print(H&);  
};  

void print(H& h) {  
    std::cout << h.data;  
}  

int main() {  
    H h;  
    print(h);  
    return 0;  
}

Options

a) 60
b) Error
c) None
d) 0

✅ Answer

a) 60

Explanation

• Friend functions can access private members
• Useful for tight coupling

---

38. Python Abstract Class

Question

How is abstraction enforced?

from abc import ABC, abstractmethod  

class I(ABC):  
    @abstractmethod  
    def must(self):  
        pass  

class J(I):  
    def must(self):  
        return "Implemented"  

j = J()  
print(j.must())

Options

a) Implemented
b) Error if not implemented
c) ABC
d) None

✅ Answer

a) Implemented

Explanation

• Abstract methods must be implemented
• Enforces abstraction contract

---

39. Debug C++ Inheritance Visibility

Question

Fix needed to access protected member?

#include <iostream>  
class Base {  
    protected:  
        int prot = 70;  
};  

class Derived : public Base {  
    public:  
        void show() { std::cout << prot; }  
};  

int main() {  
    Derived d;  
    d.show();  
    return 0;  
}

Options

a) No fix
b) Change protected to public
c) Add friend
d) None

✅ Answer

a) No fix

Explanation

• Protected members are accessible in derived classes
• Practical in controlled inheritance

---

40. Python Duck Typing (Polymorphism)

Question

What is the output?

class K:  
    def quack(self):  
        return "Quack"  

class L:  
    def quack(self):  
        return "Bark"  

def make_quack(obj):  
    return obj.quack()  

print(make_quack(L()))

Options

a) Bark
b) Quack
c) Error
d) None

✅ Answer

a) Bark

Explanation

• Duck typing depends on behavior, not class
• If it “quacks”, it works 🦆
• Core Python polymorphism concept

Jharkhand JSSC JTMACCE-2025

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