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MODEL TEST PAPER (XII AI)
10 Critical AI Concepts Explained in 5 Minutes
October 18, 2024MODEL TEST PAPER (XII AI)
October 21, 2024MODEL TEST PAPER – 1
Class – XII Subject – Artificial Intelligence
Max. Time: 2 Hours Max. Marks: 50
General Instructions:
- This Question Paper consists of 21 questions divided into two sections: Section A and Section B.
- Section A contains Objective Type Questions (24 marks), while Section B consists of Subjective Type Questions (26 marks).
- Out of the given 21 questions, you have to answer 15 questions (5 from Employability Skills and 10 from Subject Specific Skills).
- All questions in a section must be answered in the correct sequence.
SECTION A: OBJECTIVE TYPE QUESTIONS (24 Marks)
Q.1. Answer any 4 out of the given 6 questions on Employability Skills (1 x 4 = 4 marks)
i. Which of the following can help you to manage time –
a) Talking to friends
b) Making a to – do – list
c) Watching a favorite movie
d) Postpone the planned activity.
ii. Which of the following is not a quality of a self-motivated person?
a) Focused
b) Positive
c) Authoritative
d) Dedicated
iii. ________is the shortcut to closing a workbook in Open Office Calc.
a) CTRL+O
b) CTRL+S
c) CTRL+R
d) CTRL+W
iv. When the listener judges the content of the message it is called ______?
a) Understanding
b) Evaluating
c) Responding
d) Remembering
v. Which of the following is a Presentation Software?
a) MS Office Impress
b) Presentation Point
c) Client Office Impress
d) Libre Office Impress
vi. Feeling extremely nervous and worried because you believe that other people do not like 1
you or they are trying to harm you, which kind of personality disorder is this?
a) Schizoid
b) Paranoid
c) Avoidant
d) Obsessive
Q.2. Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. The first step in telling an effective data story is:
a) Visualizing data
b) Understanding the audience
c) Creating a narrative
d) Gathering data
ii. In AI projects, which function predicts a numerical value?
a) Regression
b) Classification
c) Clustering
d) Anomaly Detection
iii. Which of the following is not an advantage of cross-validation?
a) Reduces overfitting
b) Computationally expensive
c) Ensures data consistency
d) Helps in generalizing models
iv. Which stage in the AI Model Life Cycle involves data collection and cleaning?
a) Design
b) Testing
c) Deployment
d) Data Preparation
v. Which is the correct order for creating a data story?
a) Visualize, Organize, Examine Relationships, Create Narrative
b) Organize, Visualize, Create Narrative, Examine Relationships
c) Examine Relationships, Organize, Visualize, Create Narrative
d) Organize, Visualize, Examine Relationships, Create Narrative
vi. In which phase of AI project cycle do you establish business objectives?
a) Scoping
b) Evaluation
c) Deployment
d) Model validation
Q.3. Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. Techniques like descriptive statistics and visualizations can be applied to datasets after data gathering to analyze the content. To close the gap, additional data collecting may be required. Identify the stage of this analytic approach:
a) Data Requirements
b) Data Gathering
c) Data Understanding
d) Data Preparation
ii. Stories create _______ experiences that transport the audience to another space and time.
a) Unpleasant
b) Tedious
c) Repetitive
d) Engaging
iii. In which phase of the AI Model Life Cycle are strategic business objectives defined and stakeholders’ expectations aligned?
a) Design
b) Scoping
c) Evaluation
d) Data Collection
iv. Assertion (A): Stories that combine statistics and analytics are more persuasive.
Reason (R): When we talk about data storytelling, data plays a central role.
Choose the correct option:
a) Both A and R are true and R is the correct explanation of A
b) Both A and R are true and R is not the correct explanation of A
c) A is true but R is false
d) A is false but R is true
v. Which of the following is NOT a feature of RMSE?
a) It tells about the accuracy of the model.
b) Higher values indicate hyperparameters need tweaking.
c) Lower RMSE values are not good for an AI model.
d) RMSE measures how evenly distributed residual errors are.
vi. After training an AI model, AI engineers then ____ it for external inference requests.
a) Evaluate
b) Test
c) Deploy
d) Redesign
Q.4. Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. Data validation for human biases is conducted in which phase of the AI Model Life Cycle?
a) Scoping
b) Data Collection
c) Design
d) Testing
ii. Which of the following is a disadvantage of cross-validation?
a) Provides insight into how models generalize
b) It is computationally expensive
c) Gives more accurate model predictions
d) Results in more precise models
iii. Hyperparameters are parameters whose values govern the learning process.
a) True
b) False
iv. Arrange the following steps for finding compelling stories in data:
- Visualize the data
- Examine data relationships
- Organize the data
- Create a simple narrative
a) 1-2-3-4
b) 3-1-2-4
c) 4-1-3-2
d) 3-1-4-2
v. The difference between Regression and Classification Loss functions is:
a) Regression predicts a quantity, and classification predicts a label.
b) Regression predicts a label, and classification predicts a quantity.
c) Regression predicts a qualitative value, and classification predicts a label.
d) Regression predicts a label, and classification predicts a qualitative value.
vi. If AI techniques are to be applied to a dataset, the data must have a ______.
a) Association
b) Pattern
c) Both a and b
d) Neither a nor b
Q.5. Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. Which phase in Design Thinking involves gathering user feedback on the prototypes?
a) Prototype
b) Test
c) Ideate
d) Empathize
ii. What type of motivation is represented by the statement: “I enjoy learning because it helps me grow”?
a) Intrinsic motivation
b) Extrinsic motivation
c) External motivation
d) Lack of motivation
iii. What key feature does a narrative add to data storytelling?
a) Graphics
b) Data analysis
c) Context
d) Visuals
iv. Match the following tools with their purpose in AI development:
A. Python 1. Pre-built AI models
B. TensorFlow 2. Machine learning library
C. IBM Watson 3. Data visualization
a) A-2, B-3, C-1
b) A-1, B-2, C-3
c) A-2, B-1, C-3
d) A-3, B-2, C-1
v. The RMSE metric is generally used for which type of problem?
a) Classification
b) Regression
c) Clustering
d) Anomaly Detection
vi. Which of the following statements about e-waste is correct?
a) It should be disposed of in landfills.
b) It should be recycled through certified partners.
c) It can be burned for energy.
d) It should be mixed with household waste.
SECTION B: SUBJECTIVE TYPE QUESTIONS (26 Marks)
Answer any 3 out of the given 5 questions on Employability Skills (2 x 3 = 6 marks)
Q. 6. What does ‘P’ stand for in the acronym ‘RESPECT’ used to ensure active listening? Explain.
Q. 7. Shikha is an elderly woman with a habit of washing her hands excessively. Suggest one way she can overcome this personality disorder.
Q. 8. Differentiate between the terms worksheet and workbook.
Q. 9. Name two government schemes that help small-scale entrepreneurs gain capital.
Q. 10. The greening of the economy presents opportunities for starting new businesses. Write two benefits of green jobs.
Answer any 4 out of the given 6 questions (2 x 4 = 8 marks)
Q. 11. Define Capstone Project. Why is it important in AI education?
Q. 12. Can MSE (Mean Squared Error) be a negative value? Give reasons.
Q. 13. What is the importance of a narrative in storytelling with data?
Q. 14. Imagine you want to create your first AI app. List questions to decompose this task.
Q. 15. Differentiate between training set and test set in machine learning.
Q. 16. Briefly explain the Design/Build phase of the AI model life cycle.
Answer any 3 out of the given 5 questions (4 x 3 = 12 marks)
Q. 17. Explain how data storytelling can bring about change. Use a diagram if necessary.
Q. 18. Consider the data:
| x | y |
| 10 | 15 |
| 12 | 18 |
| 14 | 20 |
| 16 | 21 |
Regression equation: y=2x+1
Calculate the RMSE (Root Mean Squared Error) for this data.
Q. 19. What are the key considerations for data scientists during the testing phase?
Q. 20. Explain the Cross Validation procedure and when it is better than Train-Test split.
Q. 21. (a) List the steps for creating an effective data story.
(b) Discuss the significance of using conflict in data storytelling. How can conflict enhance the narrative and drive audience engagement?
ANSWER KEY
SECTION A: OBJECTIVE TYPE QUESTIONS
Q. 1 Answer any 4 out of the given 6 questions on Employability Skills (1 x 4 = 4 marks)
i. Answer: b) Making a to – do – list
ii. Answer: c) Authoritative
iii. Answer: d) CTRL+W
iv. Answer: b) Evaluating
v. Answer: d) Libre Office Impress
vi. Answer: b) Paranoid
Q. 2 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. Answer: b) Understanding the audience
ii. Answer: a) Regression
iii. Answer: b) Computationally expensive
iv. Answer: d) Data Preparation
v. Answer: d) Organize, Visualize, Examine Relationships, Create Narrative
vi. Answer: a) Scoping
Q. 3 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. Answer: d) Data Preparation
ii. Answer: d) Engaging
iii. Answer: b) Scoping
iv. Answer: a) Both A and R are true and R is the correct explanation of A
v. Answer: c) Lower RMSE values are not good for an AI model.
vi. Answer: c) Deploy
Q. 4 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)
i. Answer: d) Testing
ii. Answer: b) It is computationally expensive
iii. Answer: a) True
iv. Answer: d) 3-1-2-4
v. Answer: a) Regression predicts a quantity, and classification predicts a label.
vi. Answer: c) Pattern
Q. 5 Answer any 4 out of the given 6 questions (1 x 5 = 5 marks)
i. Answer: b) Test
ii. Answer: a) Intrinsic motivation
iii. Answer: c) Context
iv. Answer: a) A-2, B-3, C-1
v. Answer: b) Regression
vi. Answer: b) It should be recycled through certified partners.
SECTION B: SUBJECTIVE TYPE QUESTIONS
Answer any 3 out of the given 5 questions on Employability Skills (2 x 3 = 6 marks)
Q. 6.
Answer: The ‘P’ in the acronym ‘RESPECT’ stands for Paraphrase. It means restating what the speaker has said in your own words to show understanding and ensure active listening.
Q. 7.
Answer: Shikha can overcome her OCD by engaging in cognitive behavioral therapy (CBT). This therapy helps by exposing her to the trigger (hand washing) while helping her control the compulsion gradually.
Q. 8.
Answer: A worksheet refers to a single page of cells in an Excel file, while a workbook is a collection of multiple worksheets.
Q. 9.
Answer: Two government schemes for small-scale entrepreneurs are:
- MUDRA Yojana (Micro Units Development & Refinance Agency)
- Startup India
Q. 10.
Answer: Two benefits of green jobs are:
- Lowering energy costs by promoting the use of renewable resources.
- Creating sustainable employment that contributes to environmental protection.
Answer any 4 out of the given 6 questions (2 x 4 = 8 marks)
Q. 11.
Answer: A Capstone Project is a final project where students apply their AI learning to real-world problems. It integrates their knowledge of AI concepts such as model building and data storytelling, helping them demonstrate their skills.
Q. 12.
Answer: No, MSE (Mean Squared Error) cannot be negative. It represents the average of the squared differences between actual and predicted values, and since it squares the errors, the result is always positive or zero.
Q. 13.
Answer: A narrative adds context and makes data relatable and easier to understand. It helps in drawing connections between data points and influences how the audience interprets and responds to the information.
Q. 14.
Answer: Questions to decompose the task of creating an app:
- What is the app’s main purpose?
- Who is the target audience?
- What features will the app have?
- What will the user interface look like?
- How will I test the app’s functionality?
Q. 15.
Answer: A training set is the data used to train a machine learning model, while a test set is a separate dataset used to evaluate the model’s performance.
Q. 16.
Answer: The Design/Build phase of the AI model life cycle involves data acquisition, cleaning, and preparation. It is an iterative process where the model is trained using different algorithms and refined until it meets the desired outcomes.
Answer any 3 out of the given 5 questions (4 x 3 = 12 marks)
Q. 17.
Answer:
Data storytelling combines data, visuals, and narratives to drive change. By presenting data in a story format, it engages the audience emotionally and intellectually. For example, a company showing how customer satisfaction improved after a product change can use a narrative alongside visual graphs. The diagram below shows how storytelling connects data and action:
[Data] → [Visuals] → [Narrative] → [Engagement] → [Change]
Q. 19.
Answer:
During the testing phase of AI models, data scientists must consider:
- Data bias: Ensuring that data is representative and unbiased.
- Model accuracy: Checking how well the model predicts outcomes.
- Generalization: Testing if the model performs well on unseen data.
- Validation: Cross-validating results to avoid overfitting.
Q. 20.
Answer:
Cross-validation is a technique where the dataset is split into several subsets, and the model is trained and tested on different folds to ensure it generalizes well. It is better than a simple train-test split when the dataset is small or when model tuning is crucial, as cross-validation reduces the bias introduced by a single random split.
Q. 21.
Answer:
(a) Steps for creating an effective data story:
- Understand the audience.
- Choose relevant data and visualizations.
- Highlight key insights.
- Create a compelling narrative.
- Engage the audience with visuals and storytelling.
(b) Conflict is a key element of storytelling, and it plays an important role in data storytelling as well. Conflict creates tension and curiosity, which naturally engages the audience and keeps them invested in the story. In data storytelling, conflict often arises from a problem or challenge that needs to be addressed, such as declining student performance or ineffective business strategies. By presenting conflict, the storyteller sets the stage for a resolution, making the data more compelling and relatable. For example, a data story about a company’s declining sales might present the conflict as a failure to adapt to market trends. The narrative would then focus on how data-driven decisions helped the company overcome the challenge, leading to increased sales. This kind of story not only engages the audience but also demonstrates the power of data in solving real-world problems.
