Preparing for your CBSE Class 10 Artificial Intelligence exam? Get the latest Sample Paper Artificial Intelligence Class 10 for 2025 here!
This article provides you with the official CBSE sample paper, updated syllabus, marking scheme, and expert tips to help you score 90+ marks. Practicing from these AI sample papers will strengthen your concepts, improve speed, and boost confidence for the board exam.
Topics Covered
Blue Print Sample Paper Artificial Intelligence Class 10
Board has been uploaded the sample papers for skill courses. Artificial Intelligence is one the skill subjects. Lets begin the Sample Paper Artificial Intelligence Class 10 discussion with blue print for the same.
The assessment of Articial Intelligence class 10 is divided into two parts:
- Employability Skills – 10 Marks
- Subject Specific Skills – 40 Marks
Let’s see the unit wise mark distributions:
Part A – Employability Skills
| UNIT NO. | NAME OF THE UNIT | OBJECTIVE QUESTIONS 1 Mark | SHORT ANSWER QUESTIONS 2 Marks | TOTAL QUESTIONS |
| 1 | Communication Skills – II | 1 | 1 | 2 |
| 2 | Self-Management Skills – II | 2 | 1 | 3 |
| 3 | ICT Skills – II | 1 | 1 | 2 |
| 4 | Entrepreneurial Skills – II | 1 | 1 | 2 |
| 5 | Green Skills – II | 1 | 1 | 2 |
| TOTAL QUESTIONS | 6 | 5 | 11 | |
| NO. OF QUESTIONS TO BE ANSWERED | Any 4 | Any 3 | Any 7 | |
| TOTAL MARKS | 1 x 4 = 4 | 2 x 3 = 6 | 10 MARKS | |
Part B Subject Specific Skills
| UNIT NO. | NAME OF THE UNIT | OBJECTIVE QUESTIONS 1 Mark | SHORT ANSWER QUESTIONS 2 Marks | LONG ANSWER QUESTIONS 4 Marks | TOTAL QUESTIONS |
| 1 | Unit 1: Revisiting AI Project Cycle & Ethical Frameworks for AI | 5 | 1 | 1 | 7 |
| 2 | Unit 2: Advanced Concepts of Modeling in AI | 4 | 2 | 2 | 8 |
| 3 | Unit 3: Evaluating Models | 6 | 1 | 1 | 8 |
| 4 | Unit 5: Computer Vision | 4 | 1 | – | 5 |
| 5 | Unit 6: Natural Language Processing | 5 | 1 | 1 | 7 |
| TOTAL QUESTIONS | 24 | 6 | 5 | 35 | |
| NO. OF QUESTIONS TO BE ANSWERED | Any 20 | Any 4 | Any 3 | Any 27 | |
| TOTAL MARKS | 1 x 20 = 20 | 2 x 4 = 8 | 4 x 3 = 12 | 40 MARKS | |
General Instructions Sample Paper Artificial Intelligence Class 10
- Please read the instructions carefully.
- This Question Paper consists of 21 questions in two sections: Section A & Section B.
- Section A has Objective type questions whereas Section B contains Subjective type questions.
- Out of the given (5 + 16 =) 21 questions, a candidate has to answer (5 + 10 =) 15 questions in the allotted (maximum) time of 2 hours.
- All questions of a particular section must be attempted in the correct order.
- SECTION A – OBJECTIVE TYPE QUESTIONS (24 MARKS):
- This section has 05 questions.
- Marks allotted are mentioned against each question/part.
- There is no negative marking.
- Do as per the instructions given.
- SECTION B – SUBJECTIVE TYPE QUESTIONS (26 MARKS):
- This section has 16 questions.
- A candidate has to do 10 questions.
- Do as per the instructions given.
- Marks allotted are mentioned against each question/part.
SECTION B: SUBJECTIVE TYPE QUESTIONS
Q. 6 What is a pronoun? Give an example.
Q. 7 List four steps to build self-motivation.
Q. 8 “Security break is leakage of information stored in a computer”. Explain two ways in which personal information can be lost or leaked.
Q. 9 Reema runs a small handmade soap business. Every morning, she decides what products to make, how many soaps to produce, and where to sell them. She also arranges raw materials and assigns tasks to her staff. From the paragraph, identify and write the two functions of an entrepreneur that are being described.
Q. 10 What is organic farming? Mention any two benefits of practising organic farming.
Answer any 4 out of the given 6 questions in 20 – 30 words each (2 x 4 = 8 marks)
Q. 11 What do you do in the second and third stages of an AI Project cycle?
Q. 12 Identify the type of deep learning model.
a) This deep learning model processes input images by learning to assign importance (weights and biases) to various features in the image, allowing it to distinguish one object from another.
b) This deep learning model is inspired by the structure of the human brain. It can automatically extract features from large datasets without explicit programming. Each node in the network acts like a small machine learning algorithm.
Q. 13 How is reinforcement learning different from supervised and unsupervised learning?
| Aspect | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
|---|---|---|---|
| Data Type | Labeled data (input–output pairs are known) | Unlabeled data (no predefined output) | No fixed dataset; learns from interaction with environment |
| Goal | Learn a mapping from inputs to known outputs | Find hidden patterns or structure in data | Learn a sequence of actions to maximize cumulative reward |
| Feedback | Direct and immediate (correct answer provided) | No feedback; model finds structure on its own | Indirect and delayed (reward or penalty after actions) |
| Example | Predicting marks from study hours | Grouping customers by buying behavior | A robot learning to walk or a game agent learning to win |
| Learning Process | Learns from correct examples | Learns by finding similarities or clusters | Learns by trial and error using rewards and punishments |
Q. 14 What is model evaluation in machine learning, and how does it help improve an AI model?
Q. 15 What is a “byte image” format in the context of digital images?
Q. 16 Identify the stage of NLP and explain.
We are to the zoo going tomorrow.
We are going to the zoo tomorrow.
Q. 17 Read the case study below and answer the following questions:
A school develops an AI system to shortlist students for a competitive scholarship. The algorithm is trained to prioritize students who complete online application forms quickly — assuming that faster completion reflects confidence, competence, and tech-savviness. However, students who are not fluent in English, or who do not have access to a computer at home, take longer to fill out the form. As a result, many deserving students are unfairly rejected by the algorithm.
a) Identify two reasons why the algorithm gave biased results.
b) Mention two bioethics principles that can help solve such a problem and explain how they apply.
Q. 18
a) What is the name of the learning model that works with unlabeled data to identify hidden patterns or structures in the data?
b) Name the two main categories of this learning model.
c) Explain each category briefly and provide one example for each.
Q. 19 Identify the name of the application of Machine Learning (ML) or Deep Learning (DL) being used in the following scenarios.
a) A fitness tracker uses AI to monitor your heart rate and learn what is normal for you. One evening, while you’re relaxing and watching a horror movie, the tracker suddenly flags a spike in your heart rate as an anomaly — thinking something might be wrong.
b) A wildlife camera is equipped with AI to monitor animals in a forest. It not only records movement of animals, but also identifies and classifies them into categories like “bird”, “mammal”, “insect”, etc.,
c) A smart refrigerator is equipped with an internal camera and an AI system. One of its door features a display screen that shows a real-time image of the inside, capturing all the food items on various shelves. For each item, the system not only shows its actual image but also places an AI-generated label on top of it, such as: “Milk Carton”, “Apple”, “Ice Cream”.
d) Imagine an app that helps you unlock your bike. Instead of typing a password, you simply scribble a numerical code like “5281” on your phone screen using your finger — even if your handwriting is a bit messy after a tough workout.
Q. 20 Read the following paragraph and answer the questions that follow:
A school recently tested an AI model designed to predict whether students would pass or fail their final exams. Out of 100 students, the model correctly predicted that 40 students would pass and they actually did. It also correctly identified 30 students who were going to fail. However, the model predicted that 20 students would pass, but they ended up failing. Additionally, it predicted that 10 students would fail, but they actually passed.
a) Draw the confusion matrix based on the above information. (2)
b) Calculate the accuracy of this classification model. Show your working. (1)
c) Write the total number of wrong predictions made by the model. (1)
Ans.:
a)
(½ mark each for correctly identifying and writing values of TP, FP, TN,FN)
b) Classification Accuracy
Accuracy = 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠
= 𝑇𝑃 + 𝑇𝑁𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁
= 40 + 3040 + 20 + 30 + 10
= 70100 = 0.7
Accuracy% = 70%
(½ mark for formula; ½ mark for the answer)
c) Wrong Predictions = FP+FN =20+10 =30
(½ mark for formula; ½ mark for the answer)
Q. 21 Read the following three documents and answer the questions that follow:
● Document 1: “Students love studying AI”
● Document 2: “AI is transforming education”
● Document 3: “Teachers and students explore AI tools”
After performing basic text pre-processing (removing punctuation, converting to lowercase, and tokenizing), the documents become:
● Document 1: [students, love, studying, ai]
● Document 2: [ai, is, transforming, education]
● Document 3: [teachers, and, students, explore, ai, tools]
Questions:
a) Create the dictionary (vocabulary) of unique words from all three documents.
b) Construct the document vector for Document 3 using the dictionary.
c) Explain how Bag of Words help in feature extraction.
d) Why is the order of words not considered important in Bag of Words?
Ans.:
a) Dictionary of unique words will be:
students, love, studying, ai, is, transforming, education, teachers, and, explore, tools
(1 mark for correctly listing all unique words for the vocabulary)
b) Document vector for Document 3
| students | love | studying | ai | is | transforming | education | teachers | and | explore | tools |
| 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
(1 mark for accurately constructing the document vector for Document 3 using the given vocabulary)
c) Bag of Words (BoW) helps in feature extraction by converting text documents into fixed-length numerical vectors based on the frequency or presence of words from a defined vocabulary. This allows machine learning models to process and analyze text data just like numeric input, enabling tasks such as text classification, sentiment analysis, and topic modeling.
(1 mark for clearly explaining how Bag of Words helps in feature extraction by counting word occurrences)
d) In Bag of Words, the focus is on what words appear, not how or where they appear in the sentence. The model treats text as a “bag” of words, ignoring grammar, syntax, and word order.
(1 mark for correctly stating that word order is not important in BoW as it only considers frequency or presence of words)