The CBSE Class 10 AI 417 Sample Question Paper 2020–21 with Solution is a helpful resource for students preparing for their board exams. It is designed as per the latest pattern released by the Central Board of Secondary Education and covers important topics from the Artificial Intelligence syllabus. By practicing this sample paper, students can understand the exam format, types of questions asked, and improve their time management skills.
Along with the question paper, detailed solutions are provided to help students learn the correct method of answering each question. These solutions make it easier to understand concepts and clear doubts. Regular practice with this sample paper can boost confidence and improve overall performance in the Class 10 AI (Code 417) exam.
CBSE Class 10 AI 417 Sample QP 2020-21 with Solution
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The Year-Wise CBSE Class 10 Artificial Intelligence (417) Pre-Board and Sample Question Papers with Solutions are provided at the bottom of this post.
CBSE Class 10 AI 417 Sample Question Paper Session (2020-21)
CBSE | DEPARTMENT OF SKILL EDUCATION
ARTIFICIAL INTELLIGENCE (SUBJECT CODE - 417)
Sample Question Paper (SQP) Solution for Class X (Session 2020-2021)
Max. Time: 2 Hours
Max. Marks: 50
General Instructions:
- 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 A: OBJECTIVE TYPE QUESTIONS
Q.1 Answer any 4 out of the given 6 questions on Employability Skills (1X4=4 marks)
i ___________ is the final component in the process of communication as it defines the response given by the receiver to the sender.
a) Response
b) Request
c) Feedback
d) Notice
ii _________ refers to focusing human efforts for maintaining a healthy body and mind capable of better withstanding stressful situations.
a) Mental Health
b) Emotional Health
c) Self-Management
d) Stress Management
iii Having conscious knowledge of your own self, capabilities, feelings and one’s own character is called ________.
a) Self-awareness
b) Self-motivation
c) Self-control
d) Independence
iv A ________ is a software program that attaches itself to other programs and alters their behavior.
a) Operating system
b) Firewall
c) Antivirus
d) Computer Virus
v __________ refers to recruitment, employment, selection, training, development and compensation of the employees with an organization.
a) Entrepreneurs
b) Management
c) Human Resource Management
d) Employer
vi ________ is caused when natural or a man-made disturbance disrupts the natural balance of an ecosystem.
a) Pollution
b) Damage
c) Natural disaster
d) Ecological Imbalance
Q.2 Answer any 5 out of the given 6 questions (1X5=5 marks)
i A __________ is divided into multiple layers and each layer is further divided into several blocks called nodes.
a) Neural Networks
b) Convolutional Neural Network (CNN)
c) Machine learning algorithm
d) Hidden Layers
ii The _________ canvas helps you in identifying the key elements related to the problem.
a) Problem scoping
b) 4Ws Problem
c) Project cycle
d) Algorithm
iii _______ is a domain of AI that depicts the capability of a machine to get and analyse visual information and afterwards predict some decisions about it.
a) NLP
b) Data Sciences
c) Augmented Reality
d) Computer Vision
iv ______ is defined as the percentage of correct predictions out of all the observations.
a) Predictions
b) Accuracy
c) Reality
d) F1 Score
v _________ is the sub-field of AI that is focused on enabling computers to understand and process human languages.
a) Deep Learning
b) Machine Learning
c) NLP
d) Data Sciences
vi In __________, the machine is trained with huge amounts of data which helps it in training itself around the data.
a) Supervised Learning
b) Deep Learning
c) Classification
d) Unsupervised Learning
Q.3 Answer any 5 out of the given 6 questions (1X5=5 marks)
i Expand CBT _________.
a) Computer Behaved Training
b) Cognitive Behavioural Therapy
c) Consolidated Batch of trainers
d) Combined Basic Training
ii Name any 2 methods of collecting data.
a) Surveys and Interviews
b) Rumors and Myths
c) AI models and applications
d) Imagination and thoughts
iii What is the role of modelling in an NLP based AI model?
a) Modelling in NLP helps in processing of AI model
b) Modelling is required to make an AI model
c) In NLP, modelling requires data pre-processing only after which the data is fed to the machine.
d) Modelling is used in simplification of data acquisition
iv What will be the outcome, if the Prediction is “Yes” and it matches with the Reality? What will be the outcome, if the Prediction is “Yes” and it does not match the Reality?
a) True Positive, True Negative
b) True Negative, False Negative
c) True Negative, False Positive
d) True Positive, False Positive
v Recall-Evaluation method is:
a) defined as the fraction of positive cases that are correctly identified.
b) defined as the percentage of true positive cases versus all the cases where the prediction is true.
c) defined as the percentage of correct predictions out of all the observations.
d) comparison between the prediction and reality
vi Give 2 examples of Supervised Learning models.
a) Classification and Regression
b) Clustering and Dimensionality Reduction
c) Rule Based and Learning Based
d) Classification and Clustering
Q.4 Answer any 5 out of the given 6 questions (1X5=5 marks)
i Define Machine Learning.
a) Machine learning is the study of computer algorithms that improve automatically through experience.
b) Refers to any technique that enables computers to mimic human intelligence.
c) Machine learning refers to computer systems (both machines and software) enables machines to perform tasks for which it is programmed.
d) Machine Learning refers to projects that allow the machine to work on a particular logic.
ii Give one example of an application which uses augmented reality.
Ans: Self Driving Cars
iii Differentiate between Prediction and Reality.
a) Prediction is the input given to the machine to receive the expected result of the reality.
b) Prediction is the output given to match the reality.
c) The prediction is the output which is given by the machine and the reality is the real scenario in which the prediction has been made.
d) Prediction and reality both can be used interchangeably.
iv The term Sentence Segmentation is:
a) the whole corpus is divided into sentences
b) to undergo several steps to normalise the text to a lower level
c) in which each sentence is then further divided into tokens
d) the process in which the affixes of words are removed
v Which of the following statements is true for the term Evaluation?
a) Helps in classifying the type and genre of a document.
b) It helps in predicting the topic for a corpus.
c) Helps in understanding the reliability of any AI model
d) Process to extract the important information out of a corpus.
vi Which of the following is not part of the AI Project Cycle?
a) Data Exploration
b) Modelling
c) Testing
d) Problem Scoping
Q.5 Answer any 5 out of the given 6 questions (1X5=5 marks)
i ___________ refers to the AI modelling where the machine learns by itself.
a) Learning Based
b) Rule Based
c) Machine Learning
d) Data Sciences
ii Prediction and Reality can be easily mapped together with the help of :
a) Prediction
b) Reality
c) Accuracy
d) Confusion Matrix
iii __________ is an example of Applications of Natural Language Processing.
a) Evaluation
b) Automatic Summarization
c) Deep Learning
d) Problem Scoping
iv __________ is the last stage of the AI project Life cycle.
a) Problem Scoping
b) Evaluation
c) Modelling
d) Data Acquisition
v In _________, the machine is trained with huge amounts of data which helps it in training itself around the data.
a) Machine Learning
b) Artificial Intelligence
c) NLP
d) Deep Learning
vi In ________, input to machines can be photographs, videos and pictures from thermal or infrared sensors, indicators and different sources.
a) Computer Vision
b) Data Acquisition
c) Data Collection
d) Machine learning
SECTION B: SUBJECTIVE TYPE QUESTIONS
Answer any 3 out of the given 5 questions on Employability Skills (2X3=6 marks) Answer each question in 20 – 30 words.
Q.6 Name the four main categories of Communication Styles.
Ans: Verbal, Non - Verbal, Written and Visual
Q.7 List any 4 activities that help in stress management.
Ans: (i) Positive Thinking (ii) Physical Exercise (iii) Yoga and Meditation (iv) Nature Walks (v) Vacations
Q.8 What are antivirus? Name any 2 antiviruses.
Ans: Antivirus software is a program designed to detect and remove malicious programs (viruses) from the computer.
Examples: Microsoft Security essentials, Microsoft Defender, McAfee Virus Scan, Norton AntiVirus, Quick Heal.
Q.9 Name any 4 qualities of an entrepreneur.
Ans: (i) Hard working (ii) Optimistic (iii) Independent (iv) Energetic (v) Self-confident (vi) Perseverant
Q.10 Name any 4 man-made disruptions that cause ecological imbalance.
Ans: (i) Deforestation, (ii) Degradation of Land and Soil Erosion, (iii) Overexploitation of Resources (iv) Industrial and Atmospheric Pollution (v) Faulty Mining Practices (vi) E-waste generation
Answer any 4 out of the given 6 questions (2X4=8 marks) Answer each question in 20 – 30 words.
Q.11 Give 2 points of difference between a script-bot and a smart-bot.
Ans:
| Script Bots | Smart Bots |
|---|---|
| Easy to make | Flexible and powerful |
| Work around a script with instructions stored inside them | Work on bigger databases and other resources directly |
| Mostly free and easy to integrate | Learn on their own with more data |
| No or very little language processing skills | Coding is required to take this up on board |
| Limited functionality | Wide functionality |
Q.12 Define the term Machine Learning. Also give 2 applications of Machine Learning in our daily lives.
Ans: Machine Learning: It is a subset of Artificial Intelligence which enables machines to improve at tasks with experience (data). The intention of Machine Learning is to enable machines to learn by themselves using the provided data and make accurate Predictions/ Decisions.
Applications: Machine Learning is used in Snapchat Filters, NETFLIX recommendation system.
Q.13 Differentiate between Classification and Regression.
Ans:
| Classification | Regression |
|---|---|
| This model works on a discrete dataset which means the data need not be continuous. | Such models work on continuous data. |
| For example, in the grading system, students are classified on the basis of the grades they obtain with respect to their marks in the examination. | For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc and would train the model. |
Q.14 Explain the term Neural Networks.
Ans: Neural Networks are computer systems that work like the human brain to learn and solve problems. They are made up of many small units called neurons that are connected to each other.
Each neuron receives information, processes it, and passes the result to the next neuron. By repeating this process many times, neural networks can recognize patterns, make decisions, and improve their performance with experience (data).
Q.15 Explain the term Text Normalisation in Data Processing.
Ans: The first step in data processing is called Text Normalisation. It helps to clean the text data and make it simpler to understand and use. This process reduces the complexity of the original text.
In text normalisation, we perform different steps to improve and organize the text. We usually work with text taken from many documents. All the text collected together from these documents is called a corpus.
Q.16 What is F1 Score in Evaluation?
Ans: F1 score can be defined as the measure of balance between precision and recall.
F1 Score = 2 ×
Precision × Recall
Precision + Recall
Answer any 3 out of the given 5 questions (4X3=12 marks) Answer each question in 50 – 80 words.
Q.17 Categorize the following under Data Sciences, Machine Learning, Computer Vision and NLP:
The latest technological advancements have made our lives convenient. Google Home, Alexa and Siri have been a huge help to non-tech savvy people. Features like Facial recognition and Facelock have added additional security to our gadgets. These advancements have also contributed in making our needs more approachable and convenient. Now you can even check the prices with Price comparison websites and order groceries online with chatbots. Did you know that you can even find how you are going to look when you grow old? Faceapps and Snapchat filters have made this possible!
Ans:
- Alexa, Siri-NLP, Facial Recognition - Computer Vision
- Facelock - Computer Vision
- Price comparison websites - Data Sciences
- Chatbots - NLP
- Faceapps - NLP
- Snapchat Filters - Machine Learning
Q.18 Create a 4W Project Canvas for the following:
As more and more new technologies get into play, risks will get more concentrated into a common network. Cybersecurity becomes extremely complicated in such scenarios and goes beyond the control of firewalls. It will not be able to detect unusual activity and patterns including the movement of data.
Think how AI algorithms can scrape through vast amounts of logs to identify susceptible user behaviour. Use an AI project cycle to clearly identify the scope, how you will collect data, model and evaluation parameters.
Ans:
| OUR | [stakeholders] People who are using the new technology | WHO |
|---|---|---|
| HAS/ HAVE PROBLEM THAT | [issue, problem, need] Cyber security is the need when so much of the flow of data is not monitored or escapes the antiviruses/ firewall systems. | WHAT |
| WHEN/ WHILE | [context/situation] The problem is in the use of the latest technology where vast amounts of data is at risk. | WHERE |
| AN IDEAL SOLUTION WOULD | [benefit of solution to them] An effective AI system which is able to detect the flow of data and also report unusual activity | WHY |
Q.19 Differentiate between stemming and lemmatization. Explain with the help of an example.
Ans: Stemming is the process in which the affixes of words are removed and the words are converted to their base form.
In lemmatization, the word we get after affix removal (also known as lemma) is a meaningful one. Lemmatization makes sure that lemma is a word with meaning and hence it takes a longer time to execute than stemming.
The difference between the stemming and lemmatization can be depicted by the following example:
Q.20 Write the applications of NLP (Natural Language Processing). (Any four)
Ans: 1. Automatic Summarization: Automatic summarization is relevant not only for summarizing the meaning of documents and information, but also to understand the emotional meanings within the information, such as in collecting data from social media.
2. Sentiment Analysis: The goal of sentiment analysis is to identify sentiment among several posts or even in the same post where emotion is not always explicitly expressed.
3. Text classification: Text classification makes it possible to assign predefined categories to a document and organize it to help you find the information you need or simplify some activities.
4. Virtual Assistants: With the help of speech recognition, these assistants can not only detect our speech but can also make sense out of it.
Q.21 Imagine that you have come up with an AI based prediction model which has been deployed on the roads to check traffic jams. Now, the objective of the model is to predict whether there will be a traffic jam or not. Now, to understand the efficiency of this model, we need to check if the predictions which it makes are correct or not. Thus, there exist two conditions which we need to ponder upon: Prediction and Reality. Traffic Jams have become a common part of our lives nowadays. Living in an urban area means you have to face traffic each and every time you get out on the road. Mostly, school students opt for buses to go to school. Many times, the bus gets late due to such jams and the students are not able to reach their school on time.
Considering all the possible situations make a Confusion Matrix for the above situation.
Ans: Case 1: Is there a traffic Jam?
Prediction: Yes Reality: Yes
True Positive
Case 2: Is there a traffic Jam?
Prediction: No Reality: No
True Negative
Case 3: Is there a traffic Jam?
Prediction: Yes Reality: No
False Positive
Case 4: Is there a traffic Jam?
Prediction: No Reality: Yes
False Negativ
| Confusion Matrix | Reality | ||
|---|---|---|---|
| Yes | No | ||
| Prediction | Yes | True Positive | False Positive |
| No | False Negative | True Negative | |
Download Now - Class 10 AI 417 Sample Question Paper 2020-21 with Solution
