CBSE Class 10 Question Paper 2026 Artificial Intelleigence

FOR CBSE CLASS 10 EXAM PREPARATION

CBSE Class 10 2026
Question Paper ·
Artificial Intelleigence
EXAM YEAR TYPE SUBJECT

CBSE Class 10 2026 Question Paper Artificial Intelleigence

Notes · Sample Papers · Previous Year Papers · Mock Tests

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m .co s e m
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Series : 2KNLM Set – 4

 .
- 
Q.P. Code 104
Roll No.
 -   - 
-    
m
m Candidates must write the Q.P. Code
.co
.co on the title page of the answer-book.
s e m
s em l a
g la   {} ag
a ARTIFICIAL INTELLIGENCE
  : 2    : 50
Time allowed : 2 hours Maximum Marks : 50

       -    23  

 -        -    -  -   
m
       -  21   

m .co
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         , -        
s
 
g la
  -     15 a      -     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.
 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-book at
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.co m
the given place before attempting it.

m  15 minute time has been allotted to read this question paper. The question s e
s e paper will be distributed at 10.15 a.m. From 10.15 a.m. to 10.30 a.m., the
g l a
g la candidates will read the question paper only and will not write any answer a
a 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)  /          

 – 
(   )

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) .co
Out of the given (5 + 16) = 21 questions, a candidate has to answer
m s e m
(5 s+e10) = 15 questions in the allotted (maximum) time of 2 hours.
g l a
l a a
(v) g All questions of a particular section must be attempted in the correct
a
order.
(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.
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(d)
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Marks allotted are mentioned against each question/part.
(vii)
s em
Section-B : Subjective Type Questions (26 Marks) :

la
(a) This section has 16 questions.
g
a do 10 questions.
(b) A candidate has to
(c) Do as per the instructions given.
(d) Marks allotted are mentioned against each question/part.

Section – A

(Objective Type Questions) m
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c.1.o Answer any 4 out of the given 6 questions on Employability sskills
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s em (i) Identify the imperative sentence : g l a
la a 1
ag (A) Shut the front door. (B) She is talented artist.

(C) Are you feeling better ? (D) You were amazing !

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(ii)    -      _________    1

(A)  (B)  
(C)  (D) 

(iii) ‘ ’      1

(iv)        ? 1

(A)   (B)    
(C)     (D) ,    

(v)           – 1

 ___________  , __________  

(vi)               1

(A)  (B)   
(C)   (D)  

2.   6     5     : 51=5

(i)                
      (Computer Vision Task)     
      ,     _______  1

(A)  (Segmentation)
(B)  (Classification)
(C)  +  (Classification + Localization)
(D)     (Object Detection)

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m
m .co

m .co s e m
se g l a
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(ii) Breaking down big goals into smaller parts will make the goal
_________. 1

(A) specific (B) measurable

(C) achievable (D) realistic

(iii) Define the term ‘Time Management’. m
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1
m
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(iv) What should a strong password consist of ? 1
s e m
s em l a
gl(B)a Numbers and special characters
(A) Only letters
ag
a
(C) Name of a person

(D) Letters, numbers and special characters

(v) A misconception about an entrepreneur is : 1

Entrepreneurs are ___________, not __________.
o m
c
(vi) Choose the option which is NOT .a Sustainable Development Goal
according to United Nations. se
m
la (B) No poverty
1

(A) Population a g
(C) Quality education (D) Reduced Inequalities

2. Answer any 5 out of the given 6 questions : 51=5

(i) Anita and Surjit are creating an AI application that will classify
different types of fruits. The Computer Vision task that will identify
o m
m the type of fruit and assign a label to it is called _________. .c
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1
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e m (A) Segmentation
las
las a g
ag (B) Classification

(C) Classification + Localization

(D) Object Detection

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(ii)      : 1
     ,         
“”          ?
(A)  (Redundancy)
(B) -  (context-dependent meaning)
(C)   (Grammatical structure)
(D)   (Temporal change)

(iii)   (Ethical frameworks)          ? 1
(A)   (AI algorithms)    
(B)               
(C)      
(D)       

(iv)  1 :  (overfitting)         (learning
patterns)      (training data)      1
 2 :               
      
(A)     
(B)     
(C)  1  ,   2   
(D)  1  ,   2   

(v)          AI model       
 (learning approach)    ? 1
(A)   (Supervised Learning)
(B)   (Unsupervised Learning)
(C)   (Transfer Learning)
(D)   (Reinforcement Learning)
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m
m .co

m .co s e m
se g l a
a
(ii) Consider the following sentence : 1
On seeing her son’s result, Pooja’s face turned red with anger.

The word “red” demonstrates which characteristic of natural
language ?

(A) Redundancy
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(B) Context-dependent meaning
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(C) Grammatical
s e structure
l a
g la Temporal change
(D) ag
a
(iii) Ethical frameworks are primarily designed to : 1

(A) Increase the efficiency of AI algorithms.

(B) Ensure that choices made do not cause unintended harm.

(C) Reduce the cost of AI development.

m
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(D) Speed up the AI project cycle.

s em
(iv) Statement 1 : Overfitting occurs when a model memorizes the
la patterns.
training data rather thanglearning
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Statement 2 : Using the same data for training and evaluation
helps the model give accurate results. 1

(A) Both statements are correct.

(B) Both statements are incorrect.

(C) Statement 1 is correct but statement 2 is incorrect.
m
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m .co s em
s e latraining an AI
(v) Which learning approach would be most suitable for
g
g la model to park the car correctly ? a 1
a (A) Supervised Learning (B) Unsupervised Learning

(C) Transfer Learning (D) Reinforcement Learning

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(vi) AI                ? 1

(A)   (Data exploration)
(B)  (Modelling)
(C)  (Evaluation)
(D)  (Deployment)

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

(i)     (autonomous vehicle safety systems)   ,
           ? 1

(A)   (      )
(B)   (    )
(C)   (      )
(D)   (       )

(ii)     (byte image format)     (pixel values) 
   ? 1

(A) 0  100 (B) 0  255

(C) 1  256 (D) – 128  127

(iii)  -  “    X    Y  ” 
 (recommend)             
    : 1

(A)   (Classification model)
(B)   (Regression model)
(C)   (Association model)
(D)   (Clustering model)
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m
m .co

m .co s e m
se g l a
a
(vi) Which stage of the AI Project Cycle involves testing the model on
newly fetched data ? 1

(A) Data Exploration (B) Modelling

(C) Evaluation (D) Deployment
m
c o m m .co
3. Answer any 5 .out of the given 6 questions : 5  1 = 5 se
s em g l a
(i) In
g lathe context of autonomous vehicle safety systems, which type ofa
a error would be most critical to minimize ? 1

(A) False Positive (detecting danger when there isn’t any)

(B) False Negative (failing to detect actual danger)

(C) True Positive (detecting danger correctly)

m
.co
(D) True Negative (correctly identifying that there is no danger)

s em
la
(ii) What is the range of possible pixel values in a byte image format ? 1

(A) 0 to 100 ag (B) 0 to 255

(C) 1 to 256 (D) –128 to 127

(iii) An e-commerce platform analyzes customer purchase patterns to
recommend “Customers who bought product X also bought product

Y.” This uses : m
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1
m
m .co (A) Classification model
s e m
s e g l a
g la (B) Regression model
a
a (C) Association model

(D) Clustering model

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a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 9 of 24

(iv)  AI       /          ,
        AI       
        (Key factor)    AI  
      -        ? 1
(A)    (Intuition and values)
(B)   (Algorithm efficiency)
(C)    (Data storage capacity)
(D)   (Processing speed)

(v)   NLP  - (real time)    (Natural
speech)   (text)       ? 1
(A)    (Keyword Extraction tool)
(B)        
(C) -  :   (Auto generated captions on YouTube)
(D)   (raw text)  -  (pre-defined)     

(vi)   (Supervised learning) ,   (testing dataset) 
   ? 1
(A)     
(B)    (accuracy)    
(C)    
(D)     

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

(i)     ,   “Fire Present”      
     _____         1

(A)   (TP) (B)   (TN)
(C)   (FP) (D)   (FN)

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m
m .co

m .co s e m
se g l a
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(iv) As AI is essentially being used as a decision making / influencing
tool, we need to ensure that AI makes morally acceptable
recommendations. Which of the following is a key factor that can
knowingly or unknowingly influences our decision-making while
designing an AI model ? 1
m
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(A) Intuition and Values (B) Algorithm efficiency
m .co
. s e
s em
(C) Data storage capacity (D) Processing speed
g l a
l a a
(v) gWhich NLP application helps in converting natural speech into text
a
in real time ? 1

(A) Keyword Extraction tool

(B) Translation of books from English to Hindi language

(C) Auto generated captions on YouTube
m
(D) Classifying raw text into pre-defined groups
m .co
s e
g la is the purpose of the testing dataset ?
(vi) In supervised learning, what 1

(A) To train the model.a
(B) To evaluate the model’s accuracy.

(C) To create new features.

(D) To label the data.

m
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c. o Answer any 5 out of the given 6 questions :
4.
s em
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las (i) In a fire alarm system, if the model predicts “Fire
ag
ag there is actually no fire, this is classified as : 1

(A) True Positive (TP) (B) True Negative (TN)

(C) False Positive (FP) (D) False Negative (FN)

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(ii)  (A) : - (Bioethics) AI      (value-
based framework)      1
 (R) : - (Bioethics) ,       
   
(A) (A)  (R)     (R), (A)     
(B) (A)  (R)   ,  (R), (A)      
(C) (A)  ,  (R)   
(D) (A)  ,  (R)   
(iii)      (regression)       ? 1
(A)     -  (spam)  
(B)        (grouping) 
(C)       
(D)      
(iv)     (pixels)    (image resolution)  
       ? 1
(A)           
(B)            
(C)       ()     
(D)               
(v)  (Precision)         : 1
(A)     (observation)   
(observation)  
(B)     (observation)   
  (observation)  
(C)     (observation)   
(observation)  
(D)   (true positive)    (true negative) 
  (harmonic mean)

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(ii) Assertion (A) : Bioethics is an example of a Value-based
Framework for AI.
Reason (R) : Bioethics deals with ethical issues related to
health, medicine, and biological sciences. 1
(A) Both (A) and (R) are true and (R) is the correct explanation of
(A). m
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.co
(B) Both (A) and (R) are true, but (R) is not the correct explanation
e m
of (A).
e m l as
s
(C)a (A) is true, but (R) is false.
l ag
g
a (D) (A) is false, but (R) is true.
(iii) Which scenario best represents a regression problem ? 1
(A) Identifying whether an email is spam.
(B) Grouping customers by behaviour.
(C) Predicting tomorrow’s temperature.
m
.co
(D) Recognizing faces in photos.

em
(iv) Which of the following best describes the relationship between pixels
s
and image resolution ?
g la 1
(A) More pixels resultain lower image quality.
(B) Pixels and resolution are unrelated concepts.
(C) Resolution depends only on image file size.
(D) The number of pixels in an image is known as resolution.

(v) Precision is defined as : 1
m
m (A) The ratio of correctly predicted positive observations to total
.co
m .co observations.
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(B) The ratio of correctly predicted positive observations to total

g la predicted positive observations. a
a (C) The ratio of correctly predicted negative observations to total
observations.
(D) The harmonic mean of true positives and true negatives.
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a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 13 of 24

(vi)      (chat bot)    (coding)     
    (databases)       ? 1

(A)   (Script bot)
(B)   (Smart bot)
(C)   (Traditional bot)
(D) -  (Rule-based bot)

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

(i)    (price comparison)       AI 
(domain)    ? 1

(A)   (Computer Vision)
(B)    (Natural Language Processing)
(C)   (Statistical Data)
(D)  (Robotics)

(ii)  (A) :   (text pre-processing)     
(lower case)       1

 (R) :        “Hello”  “hello”    
   
(A) (A)  (R)     (R), (A)     

(B) (A)  (R)   ,  (R), (A)      

(C) (A)  ,  (R)   

(D) (A)  ,  (R)   

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(vi) Which type of chat bot requires coding and works on bigger
databases directly ? 1

(A) Script bot

(B) Smart bot

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(C) Traditional bot
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(D) Rule-based bot
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5. a any 5 out of the given 6 questions :
Answer 51=5

(i) Which AI domain would be most suitable for developing a price
comparison website ? 1

(A) Computer Vision

(B) Natural Language Processing
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(C) Statistical Data .co
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(D) Robotics

a g
(ii) Assertion (A) : Converting text to lowercase is preferable in text
preprocessing.

Reason (R) : It ensures that “Hello” and “hello” are treated as
the same word by the machine. 1

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

(D) (A) is false, but (R) is true.

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(iii)      (Computer Vision vs Image Processing) 
          ? 1

(A)           

(B)            

(C)       (superset)  

(D)       (superset)  

(iv)      : 1

  (machine learning)   (error)       
          (predict)    

(v)  ,    (satisfaction level)       
(reviews)           NLP  

  ? 1

(A)   (Text classification)

(B)   (Sentiment analysis)

(C)   (Keyword extraction)

(D)   (Language translation)

(vi)  AI   1000            (TP)
= 200,   (TN) = 600,   (FP) = 100,  

(FN) = 100 ,     (Predictions)   ? 1

(A) 300 (B) 600

(C) 800 (D) 900

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(iii) In the context of Computer Vision vs Image Processing, which

statement correctly differentiates them ? 1

(A) Computer Vision and Image Processing are exactly the same.

(B) Computer Vision enhances the image while Image Processing
m
does not.
c o m m .co
m . Vision is a superset of Image Processing.
(C) Computer s e
s e l a
g a
l Image Processing is a superset of Computer Vision.
(D) ag
a
(iv) State True or False : 1

In machine learning, the error is used to see how accurately the

model can predict data.

m
(v) .co
A company wants to analyze customer reviews to understand
m
s e
satisfaction levels. Which NLP application would be most suitable ? 1
g la
(A) Text classificationa

(B) Sentiment analysis

(C) Keyword extraction

(D) Language translation

o m
m c
m. (TP)
c. o (vi) An AI model was tested with 1000 test samples. If TruesePositive
s em = 200, True Negative (TN) = 600, False Positive (FP)
g l a = 100, False

l a Negative (FN) = 100, how many total predictionsawere correct ?
ag
1

(A) 300 (B) 600

(C) 800 (D) 900
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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

-
(   )

     5     3         
20-30     32=6

6.    C’s   2

7. (a)     ?
(b)            1+1=2

8.    -        ? 2

9.         2

10. ‘ ’      2

  6     4    20-30     42=8

11. - (sector-based)  - (value-based)  
(ethical frameworks)            2

12.   (Classification Model)       2

13.  RGB  (images)       ? 2

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m
m .co

m .co s e m
se g l a
a
Section – B

(Subjective Type Questions)

Answer any 3 out of the given 5 questions on Employability Skills in
20-30 words each. 32=6
m
m .co
6. .co
Write the 7 C’s of communication. 2
s e m
s em l a
g la ag
7. (a) a What is emotional intelligence ?

(b) Name any two skills included in emotional intelligence. 1+1=2

8. How ICT skills help us in our day-to-day activities ? 2

m
9. .co
State the qualities to become a successful entrepreneur. 2

s em
g la
10. Define the term ‘SustainableaDevelopment’ 2

Answer any 4 out of the given 6 questions in 20-30 words each. 42=8

11. Explain the difference between sector-based and value-based ethical
m
c om
frameworks with one example of each.
m.co 2

m . s e
s e l a
a 12. Give any two characteristics of a Classification Model.ag
l 2
ag
13. How do computers store RGB images ? 2

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

14.  (supervised)   (unsupervised)     
  2

15.    (machine learning algorithm)    -  
(Train-test split technique)   2

16.  (Stemming)   (Lemmatization)     ?  
“Wolves”    (Stemming)    (Lemmatization)  
    2

  5     3    50-80     3  4 = 12

17.   (Deep Learning),   (Artificial Intelligence) 
  (Machine Learning)      AI, ML  DL   
    (labelled)  (Venn)     4

18.   (scenarios)           
   AI     ? 4

(A)  AI    (platform)         
            (Grammar), 
 (content quality)           
(B)           AI    
  (peak traffic hours)           
(scan)              

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m
m .co

m .co s e m
se g l a
a
14. Give two differences between Supervised and Unsupervised learning. 2

15. Explain Train-test split technique with respect to machine learning
algorithm. 2
m
c o m m .co
. s e
s em
16. How is Stemming different from Lemmatization ? Explain how the word
l
g2 a
l a
“Wolves” would be processed by stemming and lemmatization. a
ag
Answer any 3 out of the given 5 questions in 50-80 words each. 3  4 = 12

17. Differentiate between Deep Learning, Artificial Intelligence and Machine
m
.co
Learning. Also draw a labelled Venn diagram depicting the relationship
m
between AI, ML and DL.
s e 4

g la
a
18. Consider the following scenarios and identify which AI domain would be
most appropriate for each, with justification : 4

(A) An AI based education platform needs to translate to English
language and analyze thousands of student essays to provide instant
m
m feedback on grammar, content quality and writing style.
c. o (B) An AI based application installed on a busy crossing m in a .co
s e
s em g l a that crossing
la metropolitan city scans all vehicles driving
a
through
ag during peak traffic hours and categorizes them into four wheelers
and two wheelers.

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.co s e m
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a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 21 of 24

19.              :
PQR Security Solutions  -          
AI                
         1500      (data
set)       ,      1000       
    250           200    
           50        
  
(A)         (Confusion matrix)    2
(B)         (True Negative)  ? 1

(C)  (Precision)     1

20. (A) CNN  ANN            3
(B)      (Neural network)  - ,   (layer) 
   1   2     : 1

21.      : 4
Document 1 : Data Science requires information.
Document 2 : Information analysis requires data.
   (document vector table)       (Bag of
words)         
__________

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m
m .co

m .co s e m
se g l a
a
19. Read the following paragraph and answer the questions that follow :
PQR Security Solutions has designed an AI Model to detect cyber attacks
on E-Commerce websites. For this, various network activities were
monitored and analyzed on one of the websites. The model was tested on a
dataset of 1500 network activities. Out of these, the model correctly
predicted that 1000 were cyber attacks. It also correctly identified that m
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250 were not cyber attacks. However, the model predicted that 200 were .co
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cyber attacks but actually they were not. Additionally, it predicted that 50
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were not cyber attacks but they actually were.
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(B) How many total cases are True Negative in the above scenario ? 1
(C) Calculate Precision. 1

20. (A) Expand and define the terms CNN and ANN. 3
(B) In the diagram of neural network given below, identify the layer that
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should be depicted in Box 1 and Box 2 : 1

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21. Consider the following documents :
c. o Document 1 : Data Science requires information. m.co 4

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g la Document 2 : Information analysis requires data.
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a Implement all the four steps of Bag of Words (BoW) model to create a
document vector table.
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