CBSE Class 10 Artificial Intelligence 2023 Question Paper

2. Answer any 5 out of given 6 questions :

(i) Two popular examples of pocket assistants are _____ and _____ .

(ii) This is a fact that all human beings have all nine types of intelligences, but at different levels. Name any two such intelligences.

(iii) Identify the incorrect statements from the following :

  1. AI models can be broadly categorized into four domains.
  2. Data sciences is one of the domain of AI model.
  3. Price comparison websites are examples of data science.
  4. The information extracted through data science can be used to make decision about it.
  1. Only (iv)
  2. (iii) and (iv)
  3. Only (i)
  4. (ii) and (iii)

(iv) During Data Acquisition, feeding previous data into the machine is called :

  1. Training Data
  2. Predicting Data
  3. Testing Data
  4. Evaluating Data

(v) Regression is one of the type of supervised learning model, where data is classified according to labels and data need not to be continuous. (True / False)

(vi) Which of the following is defined as the measure of balance between precision and recall ?

  1. Accuracy
  2. F1 Score
  3. Reliability
  4. Punctuality

3. Answer any 5 out of given 6 questions.

(i) Email filters, spam filters, smart assistants are the examples of :

  1. Pocket Assistants
  2. CV
  3. NLP
  4. Evaluation

(ii) Select the correct features of Smart Bot :

  1. Smart-bots are flexible and powerful
  2. Coding is required to take this up on board
  3. Smart bots work on bigger databases and other resources directly
  4. All of the above

(iii) For _____ the whole corpus is divided into sentences. Each sentence is taken as a different data so now the whole corpus gets reduced to sentences.

  1. Text Regulation
  2. Sentence Segmentation
  3. Tokenisation
  4. Stemming

(iv) ______ helps to find the best model that represents our data and how well the chosen model will work in future.

(v) While evaluating a model’s performance, recall parameter considers ______ .

(i) False positive (ii) True positive
(iii) False negative (iv) True negative

Choose the correct option :

  1. only (i)
  2. (ii) and (iii)
  3. (iii) and (iv)
  4. (i) and (iv)

(vi) With reference to NLP, consider the following plot of occurrence of words versus their value :

In the given graph, X represents :

  1. Rare / valuable words
  2. Punctuation words
  3. Popular words
  4. Pronoun

4. Answer any 5 out of given 6 questions.

(i) Which of the following is a feature of document classification ?

  1. Helps in classifying the type and genre of a document.
  2. Helps in creating a document.
  3. Helps to display important information of a corpus.
  4. Helps in including the necessary words in the text body.

(ii) Two conditions when prediction matches with the reality are true positive and ______ .

(iii) Which of the following is the correct feature of Neural network ?

  1. It can improve the efficiency of two models.
  2. It is useful with small dataset.
  3. They are modelled on human brains and nervous system.
  4. They need human intervention.

(iv) With reference to AI domain, expand the term CV.

(v) Under _____, one looks at various parameters which affect the problem we wish to solve, as this would make many lives better.

(vi) In this learning model, the data set which is fed to the machine is labelled. Name the model.

5. Answer any 5 out of given 6 questions.

(i) _______ is a term used for any word or number or special character occurring in a sentence. (Token / Punctuator)

(ii) When the prediction matches the reality, the condition is termed as ______ .

(iii) Smart Assistants such as Alexa, Siri are the examples of :

  1. Natural Language Processing
  2. Data Science
  3. Machine Learning
  4. Computer Vision

(iv) 4Ws Problem Canvas is a part of :

  1. Problem Scoping
  2. Data Acquisition
  3. Modelling
  4. Evaluation

(v) It refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it.

  1. Regression
  2. Classification
  3. Clustering
  4. Dimensionality reduction

(vi) Which of the following talks about how true the predictions are by any model ?

  1. Accuracy
  2. Reliability
  3. Recall
  4. F1 score

Section - B

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

11. Explain any one example of AI bias.

12. What is Dimensionality Reduction ?

13. Define Chatbot. What are its types ?

14. Define Confusion Matrix.

15. Face lock feature of a smartphone is an example of computer vision. Briefly discuss this feature.

16. With reference to data processing, expand the term TFIDF. Also give any two applications of TFIDF.

Answer any 3 out of the given 5 questions in 50-80 words each. 

17. Ms. Sooji is a beginner in the field of Artificial Intelligence. She got confused among the core terms like Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). Many a times, these terms are used interchangeably but are they the same ? Justify your answer. Help her in understanding these terms by drawing a well labelled diagram to depict the interconnection of these three fields.

18. What is the significance of AI project cycle ? Also explain in detail about how Data Acquisition is different from data exploration.

19. Create a document vector table from the following documents by implementing all the four steps of Bag of words model. Also depict the outcome of each step.

Document 1 : Sameera and Sanya are classmates.

Document 2 : Sameera likes dancing but Sanya loves to study mathematics.

20. Will it be valid to say that not all the devices which are termed as "smart" are Al-enabled ? Justify this statement. Explain any two examples from the daily life which are commonly misunderstood as AI.

21. Recently the country was shaken up by a series of earthquakes which has done a huge damage to the people as well as the infrastructure. To address this issue, an AI model has been created which can predict if there is a chance of earthquake or not. The confusion matrix for the same is :

  1. How many total cases are True Negative in the above scenario ?
  2. Calculate precision, recall and F1 score.