CBSE Class 12 Question Paper 2026 Artificial Intelligence

FOR CBSE CLASS 12 EXAM PREPARATION

CBSE Class 12 2026
Question Paper ·
Artificial Intelligence
EXAM YEAR TYPE SUBJECT

CBSE Class 12 2026 Question Paper Artificial Intelligence

Notes · Sample Papers · Previous Year Papers · Mock Tests

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Series : P3SQR Set – 4

367
- 
 . Q.P. Code
Roll No.
 -   - 
-    
m
m Candidates must write the Q.P. Code
.co
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s e m
s e l a
g l a   {} ag
a
ARTIFICIAL INTELLIGENCE
  : 2    : 50
Time allowed : 2 hours Maximum Marks : 50

       -    23  
 -        -    -  - 
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  c. o
       -  21   m
s e-        
         
g l a ,
  a
  -     15        -     10.15 
   10.15   10.30     -       
 -      
 Please check that this question paper contains 23 printed pages.
 Q.P. Code given on the right hand side of the question paper should be
written on the title page of the answer-book by the candidate.
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 Please check that this question paper contains 21 questions.
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 Please write down the serial number of the question in the answer-
e m
e m book at the given place before attempting it.
l as
las  15 minute time has been allotted to read this question paper. The question
paper will be distributed at 10.15 a.m. From 10.15 a.m. to 10.30 a.m., the ag
ag candidates will read the question paper only and will not write any answer
on the answer-book during this period.

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  :
(i)       
(ii)  -     21   : -  - 
(iii) -      ,  -       
(iv)   (5 + 16) = 21   ,    2    ()  
(5 + 10) = 15      
(v)                 
(vi) - :     (24 ) :
(a)    5   
(b)      
(c)       
(d)  /       
(vii) - :     (26 ) :
(a)    16   
(b)    10    
(c)       
(d)  /       

 –  (24 )
(   )
1.      6     4     (4  1 = 4)

(i)     
             , 
___________      1

(A)  (B) 
(C)  (D) 

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General Instructions :

(i) Please read the instructions carefully.
(ii) This question paper consists of 21 questions in two Sections : Section – A
& Section – B.
(iii) Section – A has Objective type questions whereas Section – B contains
m
Subjective type questions.
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(iv) Out of the given (5 + 16) = 21 questions, a candidate has to answer e m
s em= 15 questions in the allotted (maximum) time of 2 hours. glas
(5 + 10)
(v) g laquestions of a particular section must be attempted in the correct order.
All a
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(vi) Section – A : Objective Type Questions (24 marks)
(a) This section has 5 questions.
(b) There is no negative marking.
(c) Do as per the instructions given.
(d) Marks allotted are mentioned against each question/part.
m
(vii) Section – B : Subjective Type Questions (26 marks)
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(a)
s em
This section has 16 questions.

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(b) A candidate has to do
a questions.
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(c) Do as per the instructions given.
(d) Marks allotted are mentioned against each question/part.

Section – A (24 Marks)
(Objective Type Questions)
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1.
m Answer any 4 out of the given 6 questions on Employability Skills. c. o(4  1 = 4)
c. o (i) A statement which conveys the exact message that yousearem trying to
s em g l a
la convey to the other person is called ___________
a
statement. 1
ag (A) Clear (B) Concise

(C) Accurate (D) Active

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(ii)            ? 1
(A)        
(B)          
(C)       
(D)        

(iii)       ? 1

(iv) _________                
     1
(A)  (B) 
(C)  (D) 

(v)           ? 1
(A)   -   
(B)          
(C)    
(D)        

(vi) FIGs      1

2.   6     5     (5  1 = 5)
(i)    (retail company)        
      (data analytics)      
(underlying causes)              
         (customer behaviour
patterns),   (website traffic)     (product
return rates)               
(data analytics)      ? 1
(A)   (Descriptive Analytics)
(B)   (Diagnostic Analytics)
(C)   (Predictive Analytics)
(D)   (Prescriptive Analytics)

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m .co s e m
se g l a
a
(ii) Which of the following is NOT related to positive attitude ? 1
(A) It makes a person happier
(B) Helps to build and maintain relationships
(C) Decreases one’s chances of success
(D) Helps to make better decisions
m
m .co
.co
(iii) How does the intrinsic motivation occur ? 1
e m
em are like new pages, which are added to separate different glas
(iv) _________
s
la in a presentation.
topics
g a 1
a (A) Text (B) Document
(C) File (D) Slides

(v) Which of the following is NOT a characteristic of entrepreneurship ? 1
(A) It is a non-economic activity.
(B) It deals with optimisation in utilisation of resources.
m
(C) Ability to take risks.
.co
em
(D) Identifying an opportunity.
s
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(vi) Write the expanded formgof FIGs.
a 1

2. Answer any 5 out of the given 6 questions. (5  1 = 5)
(i) A retail company notices a sudden decline in online sales during the
last quarter. The data analytics team decides to investigate the
underlying causes of this drop. They begin examining customer
behaviour patterns, website traffic, and product return rates to m
m identify factors contributing to the decline. Which type of data .co
.co analytics is the team primarily using ?
s e m 1
s em g l a
l a (A) Descriptive Analytics
a
ag (B) Diagnostic Analytics
(C) Predictive Analytics
(D) Prescriptive Analytics

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.co s e m
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a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 5 of 24

(ii)      (image)   ()  ,      
               ? 1

(A)  (Vectors) (B)  (Pixels)
(C)  (Kernels) (D)  (Neurons)

(iii)          (user engagement)
   (content trends)       , 
                  
 (Big Data)       ? 1

(A)   (Structured Data)
(B) -  (Semi-Structured Data)
(C)   (Unstructured Data)
(D)   (Filter Data)

(iv)    (neural network)     (component)    
   (neuron)    (input)     (activated) 
   ? 1

(A)   (Activation Function)
(B)  (Bias)
(C)  (Weight)
(D)  (Neuron)

(v)   (Generative AI)      ? 1

(A)         
(B)    ,       (class boundaries)
   
(C)         (samples)    
(D)     (redundant)    

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m
m .co

m .co s e m
se g l a
a
(ii) When a computer processes an image, it perceives it as a collection of
tiny squares. What are these tiny squares called ? 1

(A) Vectors (B) Pixels

(C) Kernels (D) Neurons

m
.co
(iii) A social media analyst is working with a large collection of audio
m
.co
files, images, and video files to study user engagement and content
e m
trends
s emon various platforms. Which type of Big Data is the analyst glas
g la with ?
dealing a 1
a (A) Structured Data
(B) Semi-Structured Data

(C) Unstructured Data

(D) Filter Data

m
.co
(iv) Which component of a neural network decides whether a neuron
m
s e
should be activated or not based on the input it receives ? 1

(A) Activation Functiongl
a
a
(B) Bias

(C) Weight

(D) Neuron

(v) What is the primary objective of Generative AI ? 1
m
m (A) To classify existing data into different categories
.co
m .co (B) To define class boundaries within existing data for classification s e m
s e g l a
g la tasks.
a
a (C) To generate new data that resembles its training samples.

(D) To delete redundant data from large datasets.

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a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 7 of 24

(vi)   (Data Storytelling)         “ 
,            (limitation)  
(bias)      ”       ? 1

(A)  (Accuracy)
(B)  (Transparency)
(C)    (Respect for Privacy)
(D)    (Story Relevance)

3.   6     5     (5  1 = 5)

(i)   (Prescriptive Analytics)     : 1

(A)         (root causes)    
 
(B)   (past data)   (patterns),  (trends)  
(anomalies)    

(C)        (forecast)  
(D)   (predictive insights)      
 (intervention)   (recommend)  

(ii)               
        ,        
                
            ? 1

(A)  (Clustering)
(B)  (Recommendation)
(C)  (Regression)
(D)   (Anomaly Detection)

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m
m .co

m .co s e m
se g l a
a
(vi) Which ethical consideration in Data Storytelling specifically
addresses the need to “Clearly cite the sources of the data, methods
used for analysis, and any limitations or biases” ? 1

(A) Accuracy

(B) Transparency
m
c o m
(C) Respect for Privacy
m .co
m . Relevance s e
(D) Story
s e l a
g l a ag
3.
a
Answer any 5 out of the given 6 questions. (5  1 = 5)

(i) The primary purpose of Prescriptive Analytics is to : 1

(A) Uncover root causes and factors contributing to specific
outcomes.

(B) Identify patterns, trends, and anomalies in past data.
o m
. c
(C) Forecast future events or behaviours.
e m
(D) Recommend specific s
g la actions or interventions based on

a
predictive insights.

(ii) A bank’s fraud detection team analyses thousands of daily
transactions to identify suspicious activities. During the analysis,
they look for unusual spending patterns or transactions that
significantly differ from a customer’s normal behaviour. This process
of finding such irregular or abnormal trends within a dataset is
m
c. o
m associated with :
m.co 1

m (A) Clustering s e
s e g l a
g la (B) Recommendation a
a (C) Regression

(D) Anomaly Detection

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.co s e m
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(iii)              
          -  (motion-sensing
cameras)    ,   ,      
      (capture)         
(computer vision process)         ? 1

(A)   (Image Acquisition)
(B)  (Preprocessing)
(C)   (Feature Extraction)
(D)    (Detection and Segmentation)

(iv)    (healthcare analytics)     ,
     (wearable devices)     
      (Big Data)     ,   
(reliable insights)         , , 
  (trustworthiness)       (scenario) 
      (characteristic)    ? 1

(A)  (Volume) (B)  (Velocity)
(C)  (Variety) (D)  (Veracity)

(v)     ,      (input data)  (layers) 
    ,  (activations)       
 (predicted output)     (actual target)   
,    ________        1

(A)   (Back Propagation)
 
(B)   (Deep Learning)
(C)   (Forward Propagation)
 
(D)  (Optimization)

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(iii) A wildlife research organization is building a computer vision system

to monitor animal movements in forests. They install motion-sensing

cameras that automatically capture photos and videos of animals in

their natural habitat for further analysis. The organization is

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currently working on which stage of the computer vision process ? 1
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(A) Image Acquisition
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(B) Preprocessing
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(C)a Feature Extraction
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(iv) A healthcare analytics firm gathers patient information from a large

number of hospitals, laboratories, and wearable devices. Before

analysing this Big Data, the company ensures the consistency,

accuracy, quality, and trustworthiness of the data to produce reliable

m
.co
insights and reports. Which Big Data characteristic is illustrated in

this scenario ?
s em 1

(A) Volume g la (B) Velocity
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(C) Variety (D) Veracity

(v) In context of Neural Networks, the process in which input data flows

through the layers, activations are computed, and the predicted output

is compared to the actual target is specifically known as _________.
m 1
m c. o
m .co (A) Back Propagation
s e m
s e (B) Deep Learning g l a
g la a
a (C) Forward Propagation

(D) Optimization

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(vi)     (visualization)       
(visual representation)       ,    

   ? 1

(A)   (Scatter Plot) (B)   (Word Cloud)
(C)   (Line Graph) (D)   (Bar Chart)

4.   6     5    : (5  1 = 5)

(i) AI     (evaluation)      ? 1

(A)          
(B)          ,    
(C)         (deploy)  
(D)           (visualize)  

(ii)       (night surveillance)      
          (captured)      
 (random dots)    (blurry patches)     
(object detection)        ,     

 (distortions)             
            ? 1

(A)     (Cropping image)
(B)   (Noise Reduction)
(C)     (Resizing image)
(D)   (Image Normalization)

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(vi) Which data visualization type provides a visual representation of
word data where word size indicates frequency and importance ? 1

(A) Scatter Plot

(B) Word Cloud
m
(C) Line Graph
c o m m .co
(D) Bar .Chart s e
s em l a
g la ag
4. a any 5 out of the given 6 questions :
Answer (5  1 = 5)

(i) What is the main purpose of evaluation in an AI project cycle ? 1

(A) To collect data for training the model

(B) To assess how well a model performs after training

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(C) To deploy the model into real-world systems

em
(D) To visualize the data used for model building
s
g la
(ii) A security company is a
designing a computer vision system for night
surveillance. The captured footage often contains random dots and
blurry patches due to low lighting. To make the images clearer before
object detection, the system applies a technique to remove these
blurry patches and distortions. Which technique of Computer Vision
process is being used by the system ? m 1
m .co
m .co (A) Cropping image
s e m
s e (B) Noise Reduction g l a
g la a
a (C) Resizing image

(D) Image Normalization

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(iii)    (Big Data Analytics)        
 (processing)        (small batches)   
             ,    
(quicker decision-making)     ? 1

(A)   (Batch processing)
(B)   (Stream processing)
(C)   (Predictive analysis)
(D)   (Descriptive analysis)

(iv)   (Alpha Innovations)     
 
         ,     (development
team)      (neural network)       
    (features)     -  (three-
dimensional arrangement)    ,     (visual
data)   (processing)            
    1

(A)    (Recurrent Neural Network)
(B)     (Feed Forward Neural Network)
(C)    (Standard Neural Network)
(D)    (Convolutional Neural Network)

(v)      (hidden layers)     (ANN) 
    ? 1

(A)     (B)    
(C)   (perceptron) (D)    

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(iii) Which type of processing used in Big Data Analytics handles small
batches of data at a time to minimize the delay between data
collection and analysis, enabling quicker decision-making ? 1

(A) Batch processing

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(B) Stream processing
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(C) Predictive analysis
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(D)
g l a Descriptive analysis
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(iv) Alpha Innovations is a company specializing in artificial intelligence
solutions. For a project, the development team of the company
decides to use a type of neural network that extracts features from
images and incorporates a three-dimensional arrangement, making
it effective for processing visual data. Identify the type of neural
m
network. .co 1

s em
la
(A) Recurrent Neural Network
g
a Network
(B) Feed Forward Neural

(C) Standard Neural Network

(D) Convolutional Neural Network

(v) What is an Artificial Neural Network (ANN) with two or more
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hidden layers known as ? 1
m
m .co (A) A Basic Neural Network
s e m
s e g l a
g la (B) A Deep Neural Network
a
a (C) A Perceptron

(D) A Connection Neural Network

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(vi)   (Variational Autoencoders – VAEs)   
           (computer programs)  
       ? 1

(A)   (generator)    (discriminator) 
(B)   (encoder)    (decoder) 
(C)     (Large Language Model)   
(Transformer) 

(D)   (recurrent)     (Convolutional
network)

5.   6     5     (5  1 = 5)

(i)  (A) :    (Social media posts)   (images)
  (structured data)     1

 (R) :   (Unstructured data)  -  
(predefined format)      

(A) (A)  (R)     (R), (A)     

(B) (A)  (R)   ,  (R), (A)      

(C) (A)  ,  (R)   

(D) (A)  (R)    

(ii)    (digital image)   (resolution)   (factor)
     ? 1

(A)   (pixel)   (assigned)     (0 
255)
(B)    (pixels)   
(C)  (bytes)   (file)   
(D)   (image acquisition)      

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m
m .co

m .co s e m
se g l a
a
(vi) Variational Autoencoders (VAEs) are computer programs designed to
learn from data in a unique way. What are their two main parts ? 1

(A) A generator and a discriminator.

(B) An encoder and a decoder.
m
(C) A Large Language Model and a Transformer.
c o m m .co
.
(D) A recurrent
m and a convolutional network. s e
s e l a
g l a ag
5.
a
Answer any 5 out of the given 6 questions. (5  1 = 5)

(i) Assertion (A) : Social media posts and images are examples of
structured data. 1

Reason (R) : Unstructured data does not follow a predefined format.

m
.co
(A) Both (A) and (R) are true, and (R) is the correct explanation of
(A).
s em
a but (R) is not the correct explanation
(B) Both (A) and (R) are ltrue,
g
of (A).
a
(C) (A) is false, but (R) is true.

(D) Both (A) and (R) are false.

(ii) The resolution of a digital image is determined by which factor ? 1
o m
m (A) The numerical value assigned to each pixel (0 to 255) .c

m .co s e m
s e (B) The number of pixels in the image
g la
g la (C) The size of the file in bytes a
a
(D) The time taken for image acquisition

367^ Page 17 {}

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 17 of 24

(iii)             ,    
 :         (frame)       
 (detect)         (bounding boxes) 
                
         : 1

(A)   (Semantic Segmentation)
(B)   (Instance Segmentation)
(C)   (Object Detection)
(D)   (Histogram Equalization)

(iv)    (Innovative Labs),     (intelligent
language models)       ,   
(text prediction)         (neural network) 
        ,        
 (previous iteration)     (error rate/loss)    
   (weights)  - (fine-tune)      
_______        1

(A)   (Forward Propagation)
 
(B)   (Activation Function)
(C)   (Back Propagation)
 
(D)   (Deep Learning)

(v)    (Large Language Models – LLMs)   (large)  
  ? 1

(A)      (GPUs)     
(B)     (code)    (datasets)      
(C)      (output)      
(D)    (models)      (layers)   

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m
m .co

m .co s e m
se g l a
a
(iii) A company is developing a smart security camera. The camera
analyses each frame to automatically identify people, vehicles, and
other objects. It marks each detected object by drawing bounding
boxes around them. This activity of identifying and locating multiple
objects of interest within the image by drawing bounding boxes is
m
called :
m 1
.co
.co
(A) Semantic Segmentation
m s e m
s e l a
g
(B)a Instance Segmentation
l ag
a (C) Object Detection
(D) Histogram Equalization

(iv) Innovative Labs, a startup focused on developing intelligent
language models, is training a neural network to improve its text
prediction accuracy. During the training process, the team uses the
m
.co
practice of fine-tuning the weights of the neural network based on
em
the error rate (loss) obtained in the previous iteration to minimize
s
g la as _______.
error. This practice is known 1
a
(A) Forward Propagation

(B) Activation Function

(C) Back Propagation

(D) Deep Learning

m
om
(v) Why are Large Language Models (LLMs) referred to as ‘large’ ?
.co 1

m . c (A) They use a large number of GPUs. s em
las e glacode.
(B) They are trained on massive datasets of textaand
ag
(C) They can only generate long text outputs.

(D) They have more layers than other models.

367^ Page 19 {}

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 19 of 24

(vi)   (Data Storytelling)    ‘’ (Visuals)    : 1
(A)   (entity)      (basic facts)    (raw
facts)   
(B)      (linear)    (coherent fashion) 
  
(C)                  
  
(D)             

 –  (26 )
(   )
     5     3    20-30     (3  2 = 6)

6.                2

7.              2

8.         2

9.      ? 2

10. -  -       2

  6     4    20 – 30     (4  2 = 8)
11.  (Classification)       (Evaluation Metrics) 
   2

12.      (images)   (preprocessing)     ?
    (High Level Processing)     ? 2

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m
m .co

m .co s e m
se g l a
a
(vi) The key element ‘Visuals’ in data storytelling serves the purpose of : 1
(A) Providing the basic facts or raw facts about an entity.
(B) Organizing the key information in a linear and coherent fashion.
(C) Representing data pictorially to convey complex information
clearly and effectively.
m
m
(D) Establishing the setting and introducing main characters of the
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data story.
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a Section – B (26 Marks)
(Subjective Type Questions)
Answer any 3 out of the given 5 questions on Employability Skills in
20-30 words each. (3  2 = 6)
6. List out the problems faced by the person who lacks in communication
skills. 2
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7. em
State any four techniques how a person can become result-oriented.
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2

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8.
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Give any four advantages of Presentation software. 2

9. Who are called Business Entrepreneurs ? 2

10. Explain the role of green-jobs in eco-tourism. 2
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c. o Answer any 4 out of the given 6 questions in 20-30 words each. m .co
s e (4  2 = 8)

s em 11. Name any four evaluation Metrics for Classification. gla 2
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12. What is the role of preprocessing images in the computer vision process ?
How is it different from High Level Processing ? 2

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a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 21 of 24

13.   (Big Data)        /     2

14.     ‘’ (bias)   ?           2

15.    (Large Language Models – LLMs)      (risks)
      (training process)    (training data) 
    2

16.   (Data Storytelling)          
         2

  5     3    50 - 80     (3  4 = 12)
17.    (Data Science Methodology)     , ‘ ’
(data collection)           (primary) 
 (secondary)             4

18.    (Big Data Analytics)         
      4

19.     (Artificial Neural Network - ANN)    
    (fundamental layers)        (nodes)
    (connection)   (assign)    (weights) 
     4

20.   (Generative AI)    (Discriminative AI)  
 ,   (training focus),        
  4

21.  (Data)  ‘ ’ (Data Visualization)      ‘
’ (Heat Map)  ‘ ’ (Candlestick Chart) 
(visualization)        4
____________

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13. Mention any two disadvantages/challenges associated with using Big
Data. 2

14. What is ‘bias’ in a neural network ? Mention any one of its functions. 2

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15. State any two risks associated with Large Language Models (LLMs) that .co
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arise from the training process or the training data.
s e m 2

s em g l a
l a a
ag the term Data Storytelling. Mention any one reason why Data
16. Define
Storytelling has become very powerful today. 2

Answer any 3 out of the given 5 questions in 50 - 80 words each. (3  4 = 12)
17. With reference to the steps of Data Science Methodology, define the
process of ‘data collection’. Also differentiate between primary and
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secondary data sources of data collection with suitable examples. 4

s em
lasteps involved in the working process of
18. List and briefly explain the four
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Big Data Analytics. a 4

19. Describe the structure of an Artificial Neural Network by explaining its
three fundamental layers, and define the role of the weights assigned to
each connection between the nodes. 4

o m
c
20.mDifferentiate between Generative AI and Discriminative AI based. on their
. co Purpose, Training Focus, Application, and Models. s e m 4
s em g l a
g la 21. Define the terms ‘Data’ and ‘Data Visualization’. Explain
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a the uses of the
‘Heat Map’ and ‘Candlestick Chart’ visualization types. 4
____________

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