Organisation : Central Board of Secondary Education (CBSE)
Class Name : 12th/HSC (Class XII)
Subject : Data Science
Download : 2025 Exam Question Paper
Website : https://www.cbse.gov.in/cbsenew/question-paper.html
CBSE Class XII Data Science Question Paper
Central Board of Secondary Education (CBSE) Class XII Data Science 2025 Exam Question Paper.
CBSE Class XII Data Science Questions
Section – A : (Objective Type Questions)
1. Answer any 4 out of the given 6 questions on Employability Skills:
(i) With reference to describing an individual’s personality, FFM stands for :
(A) Flexible Framework Model
(B) Functional Flexibility Model
(C) Focused Feedback Method
(D) Five Factor Model
(ii) Which of the following is a barrier that entrepreneurs may face while running their ventures ?
(A) Availability of skilled labours
(B) Self-confidence
(C) Unavailability of monetary resources on time
(D) Risk taking capability
(iii) Identify the active voice sentence from the following :
(A) The game was won by the home team.
(B) The report is being written by the manager.
(C) The manager is writing the report.
(D) The homework was done by the student.
(iv) The shortcut key to add a new slide in a presentation is :
(A) Ctrl + N
(B) Ctrl + Alt + N
(C) Ctrl + M
(D) Ctrl + Alt + M
(v) Which term is used for motivation that comes from within an individual rather than external rewards ?
(A) Extrinsic motivation
(B) Increment motivation
(C) Intrinsic motivation
(D) Incentive-based motivation
(vi) With respect to green jobs, an Appropriate technology is the _________ technology that is environment friendly and suited to local needs.
(A) large-scale
(B) small-scale
(C) remote-scale
(D) old-scale
2. Answer any 5 out of the given 6 questions:
(i) State whether the following statement is True or False :
In terms of data privacy, companies are considered the sole owners of user data.
(A) Univariate Analysis
(B) Bivariate Analysis
(C) Multivariate Analysis
(D) Clustering
(iii) Name the algorithm which makes predictions based on the outcomes of several decision trees.
(A) Classification algorithm
(B) K-means clustering
(C) Random Forest algorithm
(D) K-nearest neighbour algorithm
(iv) Which characteristics describe K-Nearest Neighbours (K-NN) algorithm ?
(A) Lazy learning and parametric learning
(B) Eager learning and non-parametric learning
(C) Lazy learning and non-parametric learning
(D) Eager learning and parametric learning
(v) Which type of analysis is suitable for examining the relationship between rainfall and crop growth and the amount of crop growth for a certain level of rainfall ?
(A) Classification
(B) Clustering
(C) Linear Regression
(D) Decision Tree
(vi) A website selling products uses recommendation engines to predict what products a customer is likely to purchase. This functionality is achieved through :
(A) Supervised Learning
(B) Unsupervised Learning
(C) Reinforcement Learning
(D) Natural Language Processing
3. Answer any 5 out of the given 6 questions:
(i) CCPA stands for :
(A) California Consumer Privacy Act
(B) Children’s Consumer Privacy Act
(C) California Consumer Public Act
(D) Children’s Consumer Public Act
(ii) Which of the following are common techniques for handling missing data in a dataset ?
1. Remove the entire column of data.
2. Remove the row of data with missing values.
3. Insert a value close to the mean or mode of the variable with missing data.
4. Leave the missing data as it is.
5. Insert a random value.
(A) 1 and 2
(B) 2 and 3
(C) 3 and 4
(D) 4 and 5
(iii) In case of a classification tree, predictions on unseen data are made using the _________.
(A) Mean
(B) Median
(C) Mode
(D) RMSE
(iv) How does the curse of dimensionality affect the performance of the K-Nearest Neighbours (K-NN) algorithm ?
(A) K-NN performs better with an increasing number of input variables.
(B) K-NN struggles to predict the output accurately with an increasing number of input variables.
(C) K-NN becomes faster with more input variables due to increased computational power.
(D) K-NN becomes less sensitive to outliers with an increasing number of input variables.
(v) Assertion (A) : In real-life scenarios, a small RMSE value indicates a better model fit to the data and higher accuracy.
Reason (R) : A large RMSE value suggests that the model fits the data well.
(A) Both (A) and (R) are true, and (R) is the correct explanation of (A).
(B) Both (A) and (R) are true, but (R) is not the correct explanation of (A).
(C) (A) is true, but (R) is false.
(D) (A) is false, but (R) is true
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