CBSE Class 12 Question Paper 2026 Data Science

FOR CBSE CLASS 12 EXAM PREPARATION

CBSE Class 12 2026
Question Paper ·
Science
EXAM YEAR TYPE SUBJECT DETAILS

CBSE Class 12 2026 Question Paper Science Data

Notes · Sample Papers · Previous Year Papers · Mock Tests

m
m .co

m .co s e m
se g l a
Series : QR1SP a SET-4
- 
Q.P. Code 368
 .  -   - 
Roll No. -    
Candidates must write the Q.P. Code
m
.co
on the title page of the answer-book.
{} m
.co s e m
 :
s em NOTE :
l a
a
(I) gl     -    ag
(I) Please check that this question
a
23   paper contains 23 printed pages.

(II) -        - (II) 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.
(III)       -  21    (III) Please check that this question
m
paper contains 21 questions.

m (IV) Please .co
(IV)         ,
-       a
e
s
write down the serial
number of the question in the
l 
   ag answer-book at the given place
before attempting it.
(V)  -     15    (V) 15 minute time has been allotted
    -     to read this question paper. The
question paper will be distributed
10.15     10.15   10.30
at 10.15 a.m. From 10.15 a.m. to
    -     10.30 a.m., the candidates will
    -     read the question paper only and
  m on the
will not write any answer

om
answer-book during this period.
. co
. c e m
e m as
 
l
las ag
ag DATA SCIENCE
{ZYm©[aV g‘¶ : 2 KÊQ>o A{YH$V‘ A§H$ : 50
Time allowed : 2 hours Maximum Marks : 50

368* 2403 1 * Page P.T.O.

m .
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s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 1 of 24

  :
(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)   /          

368* 2 * Page

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m
m .co

m .co s e m
se g l a
General Instructions : a
(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
c o m
Subjective Type Questions.
m .co
. given (5 + 16 =) 21 questions, a candidate has to answer se
em=) 15 questions in the allotted (maximum) time of 2 hours. gla
(iv) Out of the
(5 +s10
g la a
(v) a All questions of a particular section must be attempted in the correct
order.

(vi) Section – A : Objective Type Questions (24 marks) :

(a) This section has 5 questions.

(b) There is no negative marking.
m
(c) Do as per the instructions given.
.co
(d) s em
Marks allotted are mentioned against each question/part.
gla Questions (26 marks) :
(vii) Section – B : SubjectiveaType

(a) This section has 16 questions.

(b) A candidate has to do 10 questions.

(c) Do as per the instructions given.

(d) Marks allotted are mentioned against each question/part.
m
m .co
m .co s e m
s e g l a
g la a
a

368* 3 * Page P.T.O.

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 3 of 24

 – 
(   ) (24 )

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

(i)   _________          1

(A) 10 (B) 12

(C) 15 (D) 20

(ii)   _________  ___________       1

(iii)    -             
 ,       ? 1

(A)  (B) 
(C)  (D)  
(iv)             ? 1

(A)   ,       
(B)    ,      
(C)  ,        
(D)    ,      
(v)               , 
_________      1

(A)   (B) ..
(C)  (D) 
(vi)           __________  ____________
   14       1

368* 4 * Page

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m
m .co

m .co s e m
se g l a
Section – A a
(Objective Type Questions) (24 Marks)

1. Answer any 4 questions out of given 6 questions on Employability
Skills : 41=4

(i) There are _________ basic punctuation marks or signs used in m
m .co
.co m
English. 1
s e
(A) 10
s em (B) 12 l a
g la 15
(C) (D) 20
ag
a
(ii) Extrinsic motivation arises because of _________ or ___________. 1

(iii) Individuals who love interacting with people around and are
generally talkative can be termed as – 1

(A) Extroverts (B) Introverts

m
.co
(C) Shy (D) Scared

s em
(iv) The correct order of steps for entering data in a spreadsheet is – 1

g
(A) type the data, click thelacell and press enter
a
(B) click the cell, type the data and press enter

(C) press enter, click the cell and type the data

(D) click the cell, press enter and type the data

(v) Individuals who focus on developing solutions that benefit the society
are called _________ entrepreneurs.
m 1
m c. o
.co em
(A) First generation (B) I.T.

e m (C) Social (D) Family las
las ag
ag (vi) The Ministry of Urban Development, Government of India has
classified solid waste in 14 categories based on the __________ and
____________. 1

368* 5 * Page P.T.O.

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

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

(i)      (data privacy legislation)      
       13          
  ? 1

(A) GDPR (B) HIPAA
(C) CCPA (D) COPPA
(ii)    (dataset)  Exploratory Data Analysis (EDA)   ,
    (variable), Math_Score,         
      (outlier)           
 ? 1

(A)  (Histogram) (B)   (Box Plot)
(C)   (Bar Chart) (D)   (Pie Chart)

(iii)        (internal node)      ? 1

(A)     (class label)    
(B)      (outcome)    
(C)     (class)        
(D)        (probability)    
(iv) K-NN  (algorithm)  k     (value)    
   ? 1

(A)   (outliers)         (decision
surface)    (specific)   

(B)   (outliers)          
 (generalize)      
(C)      (memory efficiency)    
(D)   (dataset)       (prediction) 
     

368* 6 * Page

For more Question Papers, Sample Papers, Notes & Syllabus visit Page 6 of 24

m
m .co

m .co s e m
se g l a
2. Answer any 5 out of the given 6 questions. a 51=5

(i) Which data privacy legislation specifically deals with how websites
and online companies collect data from children under 13 years of
age ? 1

(A) GDPR (B) HIPAA
m
.co
(C) CCPA (D) COPPA
m
(ii) .co
Bijoy is performing Exploratory Data Analysis (EDA) on a dataset,
s e m
whichem contains only one variable, Math_Score. He wants to check if la
l as are any outliers in this variable. Which graph is most suitablea
g
agfor this purpose ?
there
1

(A) Histogram (B) Box Plot

(C) Bar Chart (D) Pie Chart

(iii) What is the primary function of each internal node in a Decision
Tree ? 1
m
(A) It holds the final class label.
.co
em
(B) It represents the outcome of a test.
s
g
(C) It denotes a question lona choosing a particular class.
a
(D) It represents the probability of success for a decision.

(iv) What is the common effect of choosing a big value of k in the K-NN
algorithm ? 1

(A) It increases the effect of outliers and makes the decision surface
more specific.
m
m (B) It reduces the effect of outliers and causes the decision surface .co
m .co to generalize.
s e m
s e g l a
g la (C) It increases the memory efficiency of the algorithm.
a
a (D) It always improves prediction accuracy regardless of the
dataset.

368* 7 * Page P.T.O.

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 7 of 24

(v)      (scatter plot)   ,    (variables),
X  Y            L    (line) 
_______    1

(A) Line of Best Fit (B) Line of Worst Fit
(C) Line of Median (D) Line of Mode
(vi)  raw                
    (data points)           
       ? 1
(A)   (Supervised Learning)
(B) - (Expectation-Maximization)
(C)  (Clustering)
(D)     (Anomaly detection)

3.   6     5      51=5
(i)      (admissions office)   - (pre-
screen)               
     (test scores)    ,      
  :     (filter out)        
 :   (automated filtering process)   
(ethical guidelines)     ? 1
(A)    (Security of Data)
(B)    (Privacy of Data)
(C)   (Non-discrimination)
(D)  (Openness)

368* 8 * Page

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m
m .co

m .co s em
se g la
a shows the relationship
(v) Consider the scatter plot shown below, that
between two variables, X and Y. The line marked as L in the graph
below is known as _______. 1

m
m .co
m .co s e m
s e l a
g l a ag
a
(A) Line of Best Fit (B) Line of Worst Fit

(C) Line of Median (D) Line of Mode

(vi) What is the name of the process of dividing entire raw data into

m
several groups such that data points in one group are similar to
o
other data points in the same group c
. but different from those in other
e m
groups ?
las 1

(A) Supervised Learninga g (B) Expectation-Maximization

(C) Clustering (D) Anomaly detection

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

(i) An admission office of a college uses an automated system to pre-

o m
screen applications. The process is designed to automatically filter out
m . c
m .co s e m
applicants who attended certain low-performing schools, regardless of

s e the individual student’s grades or test scores.
l a
g la ag guidelines ?
This automated filtering process violates which ethical 1
a (A) Security of Data (B) Privacy of Data

(C) Non-discrimination (D) Openness

368* 9 * Page P.T.O.

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 9 of 24

(ii)    (data analyst)             
 (variable)    (entries)      (data cleaning)
      (missing values)  handle    
  / ? 1

(i)        
(ii)     (row)    
(iii)     (mean)/ (mode)     insert  
(iv)     (duplicate) 
(v)      (negative numbers)    
 :
(A) (i), (ii)  (iv) (B) (ii)  (iii)

(C) (ii), (iii)  (v) (D) (iii)  (iv)

(iii)                 
        (hiking)            
    ,          ,   
             (uncertain weather
outcome)    ,       (node)     ? 1

(A)   (Square node) (B)   (Circular node)
(C)   (Triangular node) (D)   (Diamond node)

(iv)              (input
variables)     K-NN      (data point) 
   (prediction)      ? 1

(A)     (Imbalanced Data Problem)
(B)     (No Training Step)
(C)      (Sensitivity to Outliers)
(D)   (Curse of Dimensionality)
368* 10 * P a g e

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m
m .co

m .co s em
se g la
a notices that a variable
(ii) A data analyst is working on a dataset. She
has some missing entries. What is the most appropriate way(s) to
handle these missing values during the data cleaning process ? 1

(i) Leave the missing values as they are

(ii) Remove the row of the variable that is missing
m
c o m
(iii) Insert a value close to mean/mode of the variable that is
m .co
.
missing
m s e
s e l a
g a
l Duplicate the missing entries
(iv) ag
a (v) Replace the missing values with negative numbers
Options :

(A) (i), (ii) and (iv) (B) (ii) and (iii)

(C) (ii), (iii) and (v) (D) (iii) and (iv)

m
.co
(iii) Sara is creating a decision tree to plan her weekend activities. One of

em
the decisions is whether to go hiking or stay home. If she goes hiking,
s
g laor rainy, but she isn’t sure what it will
the weather could be sunny
a
be. When representing the uncertain weather outcome in her
decision tree, what type of node should Sara use ? 1

(A) Square node (B) Circular node

(C) Triangular node (D) Diamond node

m
m (iv) Which of the following describes the problem where the K-NN
.co
m .co em
algorithm struggles to predict the output of a new data point as the
s
s e number of input variables grows ?
g la 1

g la a
a (A) Imbalanced Data Problem (B) No Training Step

(C) Sensitivity to Outliers (D) Curse of Dimensionality

368* 11 * P a g e P.T.O.

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 11 of 24

(v)     (Root Mean Square Deviation)  

(square root)  ? 1
(A)    (Mean of residuals)
(B)    (Variance of residuals)
(C)   (Standard deviation)
(D)    (Mean absolute error)

(vi)   (approach)  K-   (K-means Clustering
Algorithm)             ,  
   ? 1
(A)     (Anomaly Detection)
(B) - (Expectation-Maximization)
(C)   (Decision Tree)
(D)   (Linear Regression)

4.   6     5      51=5
(i) 11 , 2019 ,   (ministry)     Personal Data
Protection Bill, 2019   ? 1
(A)     
(B)    
(C)  
(D)     

(ii)   (entering)  ,     Distance ()  (variable) 
 (numeric)    (text)          
          ? 1
(A)        ,          
(clean)      
(B)              
(C)         (mathematical operations) 
  
(D)            

368* 12 * P a g e

For more Question Papers, Sample Papers, Notes & Syllabus visit Page 12 of 24

m
m .co

m .co s em
se g la
a root of :
(v) The Root Mean Square Deviation is the square 1

(A) Mean of residuals (B) Variance of residuals

(C) Standard deviation (D) Mean absolute error

(vi) The approach that the K-means Clustering Algorithm follows to
solve a given clustering problem is called : 1 m
m .co
m .co
(A) Anomaly Detection (B) Expectation-Maximization
s e m
s e l a
ag
(C) Decision Tree (D) Linear Regression
g l a
a
4. Answer any 5 out of the given 6 questions. 51=5

(i) On 11th December, 2019, which ministry tabled the Personal Data
Protection Bill, 2019 in the Indian Parliament ? 1

(A) Ministry of Health and Family Welfare
m
(B) Ministry of Law and Justice .co
s em
(C) Ministry of Finance
g la
a and Information Technology
(D) Ministry of Electronics

(ii) While entering data, Rehan has mistakenly recorded the variable
Distance as text instead of numeric. Which of the following
statements is true for this scenario ? 1

(A) Once the data is recorded, it cannot be cleaned during any
m
m .co
.co
phase of data analysis.
e m
e m as
(B) It will cause problems while using the data to build
l a model.

las ag any issues.
ag (C) It will allow all mathematical operations without

(D) It has no effect on any type of analysis.

368* 13 * P a g e P.T.O.

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 13 of 24

(iii) K-NN  -  (non-parametric algorithm)   
 ? 1
(A)   (classification)         (specific
distribution)   
(B)        (learning parameters)  
   
(C)             
(D)         (parameters)     
   
(iv)    (linear regression)          
  (observed points),     (predicted values) 
  ,      (Root Mean Square Deviation) 
           (deviation value)    
   (recommended action)   ? 1
(A)     (dependent variable)    
(B)   (independent variable)    (dependent variable) 
  
(C)   :   (Retrain the model)
(D)      (classification algorithm)  
 
(v) -1 :   (Logarithmic functions)   (linear
functions)    
-2 : -  (non-linear regression)     (curve) 
   (follow)    1
(A) -1  -2    
(B) -1  -2    
(C) -1  ,  -2   
(D) -2  ,  -1   
(vi) K-  (K-means clustering)       
    (data classes)   (cluster)      
    ______         1
(A)     (A supervised learning technique)
(B)    (A classification technique)
(C)      (An unsupervised machine
learning technique)
(D)    (A regression technique)

368* 14 * P a g e

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m
m .co

m .co s em
se g la
(iii) Why is K-NN considered as non-parametricaalgorithm ? 1
(A) It assumes a specific distribution for the data before
classification.
(B) It requires learning parameters for the data distribution.
(C) It does not assume anything about the distribution of the data.
(D) It uses a fixed number of parameters to model the data. m
m .co
.co m
(iv) Sneha is performing linear regression. She is using the Root Mean
e
Squarem
s e Deviation method to determine how close the observed points glas
g la to the model’s predicted values. What is the recommended actiona
are
a if a large deviation value is obtained ? 1
(A) Remove the dependent variable from the model.
(B) Change the independent variable to the dependent variable.
(C) Retrain the model.
(D) Switch from linear regression to a classification algorithm.
m
.co
(v) Statement-1 : Logarithmic functions are examples of linear
functions.
e m
s
Statement-2 : The grapha of non-linear regression follows the
l
ag
equation of a curve. 1
(A) Both Statement-1 and Statement-2 are correct.
(B) Both Statement-1 and Statement-2 are incorrect.
(C) Statement-1 is correct but Statement-2 is incorrect.
(D) Statement-2 is correct but Statement-1 is incorrect
(vi) K-means clustering is a technique used to spot clusters of data m
m .co
.co em
classes in a dataset and is classified as _________. 1

em (A) A supervised learning technique
l as
las (B) A classification technique ag
ag
(C) An unsupervised machine learning technique
(D) A regression technique

368* 15 * P a g e P.T.O.

m .
.co s e m
s em l a
g la ag
a For more Question Papers, Sample Papers, Notes & Syllabus visit Page 15 of 24

5.   6     5      51=5
(i)     GDPR      (aspect)  ? 1
(A)   (Data multiplication)
(B)      (Right to be forgotten)
(C)    (Mandatory data sharing)
(D)    (Unlimited data storage)
(ii) -1 :   (Regression trees)         
(dependent variable)  (categorical)   
-2 :              (branches)  
  1
(A) -1  -2    
(B) -1  -2    
(C) -1  ,  -2   
(D) -2  ,  -1   
(iii) K-NN           ? 1
(A)     (imbalanced data)     (accurate)  
(B)    (curse of dimensionality)   (immune)  
(C)    - (memory-inefficient)  
(D)   (outliers)    (robust)  
(iv)     (simple linear regression)   Y = m * X + b
        m  b    ? 1
(A)  (Slope),   (independent variable)
(B)   (Independent variable),  (Intercept)
(C)  (Slope),  (Intercept)
(D)   (Dependent Variable),  (Slope)
(v)            (intercept) 
  (explanatory variable)    _______    1
(A)  (Co-efficient)
(B)   (Dependent variable)
(C)  (Intercept)
(D)   (Linear regression)

368* 16 * P a g e

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m
m .co

m .co s e m
se g l a
5. Answer any 5 out of the given 6 questions. a 51=5
(i) Which of the following is an important aspect of GDPR legislation ? 1
(A) Data Multiplication (B) Right to be forgotten
(C) Mandatory Data Sharing (D) Unlimited Data Storage
(ii) Statement-1 : Regression trees are used when the dependent
m
.co
variable is categorical.
m
m.co
Statement-2 : A node can have two or more branches in a decision
s e m
s etree. l1a
g la Both Statement-1 and Statement-2 are correct.
(A) ag
a (B) Both Statement-1 and Statement-2 are incorrect.
(C) Statement-1 is correct but Statement-2 is incorrect.
(D) Statement-2 is correct but Statement-1 is incorrect.
(iii) Which of the following is correct for the K-NN algorithm ? 1
(A) It is highly accurate with imbalanced data.
m
(B) It is immune to the curse of dimensionality.
m .co
s e
(C) It is slow and memory-inefficient.

g la
(D) It is robust to outliers.
a
(iv) The equation for a simple linear regression is of the form Y = m * X + b.
What do m and b in the equation ? 1

(A) Slope, Independent variable
(B) Independent variable, Intercept
(C) Slope, Intercept
m
m (D) Dependent Variable, Slope
c. o (v) A simple regression equation has an intercept on the right-hand
m side .co
s e
s em g l a
la and an explanatory variable with a/an _______.
a 1
ag (A) Coefficient (B) Dependent variable
(C) Intercept (D) Linear regression

368* 17 * P a g e P.T.O.

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

(vi) K-  (K-means clustering) ,      

  (clusters)  , K   (specifies)      
 (centroids)    (initialize)   ? 1

(A)    (data points)       (assign)  
(B)   (squared distance)       K   
(assign)  

(C)     (shuffling)     (randomly) K  
   
(D) , K        (labelled data)    

 – 
(   ) (26 )

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

6. “              ”  
   2

7.        ? 2

8.            ?       2

9. “       ”,       2

10. -          ?       2

368* 18 * P a g e

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m
m .co

m .co s em
se g la
(vi) The K-means Clustering algorithm, firstaspecifies the number of
clusters, K depending on the input. Thereafter how does it initialize
the centroids ? 1

(A) By assigning all data points to any one cluster.

(B) By computing the sum of the squared distance and assigning to
the K clusters.
m
o m
(C) By shuffling the data points and then selecting K data points
c m .co
m .
randomly. s e
s eBy using only the labelled data for clusters, K. l a
g l
(D)a ag
a
Section – B

(Subjective Type Questions) (26 Marks)

Answer any 3 out of the given 5 questions on Employability Skills in
20-30 words each.
om
32=6

6. . c
“To learn the language, one needs to develop four key skills.” Name the
e m
skills.
las 2

ag
7. What do you understand by the term motivation ? 2

8. How digital presentation can be saved on the computer ? Give any one
advantage of it. 2

o m
9.om“Entrepreneurship is the perfect combination of art and science.”
c
. Explain
c. e m
e m the given statement. l as 2

l as ag
ag 10. What do you mean by appropriate technology related to green-jobs ? Give
any two examples also. 2

368* 19 * P a g e P.T.O.

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

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

11.   (Data privacy)   ?        
 (personal identifiable information)   (encrypted)  
      ,            ? 2

12.       (cafe)            
            (correlation)    
 30                   2
(a)             (analysis)  
  ?
(b)     (visualize)         
(graphical method)     ,    

13. - (Cross-Validation)          
   2

14.   (Decision Trees)   (versatile)     ?   
 
    2

15.    (Mean Absolute Error-MAE)   ? ‘     ’
(Fitting the line to the data)      ,   MAE  
   2

16.   (Supervised Learning)    (Unsupervised
Learning)          2

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

17. HIPAA  (legislation)         : 1+2+1=4
(a) HIPAA   (abbreviation)   (expand)  
(b) HIPAA     (objectives)   
(c)   (Personal identifiers)     ,  HIPAA
   
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a words each.
Answer any 4 out of the given 6 questions in 20-30 42=8

11. What is Data privacy ? Explain why data privacy rules can be violated
even if a company stores personal identifiable information securely in an
encrypted format ? 2

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12. Raunak owns a small café and wants to understand if there is a
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(a) What type of analysis should he use to study this correlation ?
(b) Name any one graphical method he can use to visualize this data.

13. Name and explain any one popular method of cross-validation. 2

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14. Why are decision trees considered versatile ? Mention any two points. 2

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16. Mention any two differences between Supervised Learning and
Unsupervised Learning. 2

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g l a 1+2+1=4

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a (b) State any two objectives of HIPAA.
(c) Give any two examples of personal identifiers that HIPAA protects.

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18.   (Univariate Analysis)   (concept)    
   (main purpose)        - 
(Statistical)   -  (graphical)      4

19.   (Decision Tree)   ?           4

20. (a)    (health researcher)   :  (age), 
(weight),   (cholesterol level)    

 (daily exercise duration)       
(blood pressure)  predict    

           (regression model) 
             (scenario)  
   
(b)                  
(exponential) (5 )              

    (regression)       ? 3+1=4

21.  -   (real-world applications)  
 (Unsupervised Learning)   (role)    : 4

(i)   (Recommendation engines)
(ii)   (Anomaly detection)
(iii)   (Medical imaging)
(iv)   (News categorization)
_____________

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18. Explain the concept of Univariate Analysis. Describe its main purpose
and list two different statistical and two different graphical methods
associated with it. 4

19. What is a Decision tree ? Explain the steps involved in creating a decision
tree. 4
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Identify and define the most suitable regression model to analyze the
relationship. Explain why this model is appropriate for this scenario.
(b) Rudra started her business six months back. Her income has increased
exponentially (5 times) every month. Which type of regression can be

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used to predict her income in the seventh month ? 3+1=4
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21. Explain the role of Unsupervised Learning in the following real-world
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applications :
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(i) Recommendation engines
(ii) Anomaly detection
(iii) Medical imaging
(iv) News categorization
_____________
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