CBSE Class 9 Data Literacy Important Subjective Questions & Answers (2026–27) | Unit 2 Artificial Intelligence
Class 9 · Artificial Intelligence
Prepare for your CBSE Class 9 Artificial Intelligence examination with these 25 important subjective questions and answers from Unit 2: Data Literacy. These questions are based on the latest CBSE syllabus (2026–27) and are ideal for Unit Tests, Half-Yearly, Annual Examinations, competency-based assessments, and viva preparation.
Topics Covered
- Introduction to Data Literacy
- Data Pyramid (DIKW Model)
- Data Privacy and Data Security
- Data Acquisition
- Primary and Secondary Data
- Data Preprocessing
- Data Interpretation
- Data Visualization
- Qualitative and Quantitative Data
- Competency-Based & HOTS Questions
CBSE Exam Tip: Learn the Data Pyramid, different types of data, methods of data acquisition, data preprocessing techniques, and graphical representation of data. Practice competency-based questions and underline important keywords while writing answers.
Subjective Questions with Answers
Q1. Define Data Literacy. Why is it important in today's world?
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Answer:
Data Literacy is the ability to collect, understand, interpret, analyze, and communicate data effectively.
It is important because it helps people make informed decisions, solve real-life problems, think critically, identify patterns, and use data responsibly in education, business, healthcare, and everyday life.
Q2. Explain the different levels of the Data Pyramid with an example.
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Answer:
The Data Pyramid (DIKW Model) has four levels:
- Data: Raw facts without meaning.
Example: Red, Traffic Light 1 - Information: Processed data with meaning.
Example: The traffic signal is red. - Knowledge: Understanding what the information means.
Example: If I drive now, an accident may occur. - Wisdom: Making the correct decision.
Example: I should stop the vehicle.
Q3. How does Data Literacy help in informed decision-making?
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Answer:
Data Literacy enables people to collect and analyze accurate data before making decisions. Instead of depending on guesses or assumptions, decisions are based on facts, patterns, and evidence, resulting in better planning and problem-solving.
Q4. Differentiate between Data Privacy and Data Security.
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| Data Privacy | Data Security |
|---|---|
| Focuses on how personal data is collected, shared, and used. | Focuses on protecting data from unauthorized access, theft, or damage. |
| Ensures users have control over their personal information. | Uses passwords, encryption, and firewalls to secure data. |
Q5. List any four best practices for Cyber Security.
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Answer:
- Keep passwords strong and confidential.
- Do not share OTPs or banking details with anyone.
- Avoid clicking suspicious links or emails.
- Do not post sensitive personal information on social media.
Q6. What is Data Acquisition? Why is it important in AI projects?
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Answer:
Data Acquisition is the process of collecting relevant data from different sources for analysis and model training.
It is important because AI systems learn from data. Accurate, relevant, and authentic data helps develop reliable AI models and improves prediction accuracy.
Q7. Differentiate between Primary Data and Secondary Data with examples.
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| Primary Data | Secondary Data |
|---|---|
| Collected directly by the researcher. | Collected from existing sources. |
| Examples: Surveys, interviews, sensors. | Examples: Government portals, websites, Kaggle datasets. |
| Collected for a specific purpose. | Already available for public or organizational use. |
Q8. Explain the concept of Web Scraping.
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Answer:
Web Scraping is the process of automatically collecting large amounts of data from websites using software tools.
It saves time and is useful for AI projects, but developers must ensure they have permission to use the collected data and follow legal and ethical guidelines.
Q9. What is Data Augmentation?
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Answer:
Data Augmentation is a technique used to increase the size of a dataset by creating modified versions of existing data.
For example, an image can be rotated, flipped, cropped, or its brightness changed. This helps improve the performance and accuracy of AI models.
Q10. Compare Qualitative Data and Quantitative Data.
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| Qualitative Data | Quantitative Data |
|---|---|
| Describes qualities or characteristics. | Represents numbers and measurements. |
| Cannot usually be measured numerically. | Can be measured and analyzed mathematically. |
| Examples: Colour, opinion, feedback. | Examples: Height, marks, age, weight. |
Q11. Define Discrete Data and Continuous Data.
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Answer:
| Discrete Data | Continuous Data |
|---|---|
| Contains separate and countable values. | Contains measurable values within a range. |
| Cannot have fractional values. | Can have decimal or fractional values. |
| Examples: Number of students, number of books. | Examples: Height, weight, temperature. |
Q12. What are Data Features? Why are they important?
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Answer:
Data Features are the characteristics or attributes of a dataset that help an AI model understand and analyze information.
They are important because they help the model identify patterns, make predictions, and improve the accuracy of AI solutions.
Q13. Differentiate between Independent Features and Dependent Features.
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| Independent Features | Dependent Features |
|---|---|
| Input variables provided to the AI model. | Output variable that the AI model predicts. |
| Remain under the user's control. | Depends on the independent features. |
| Examples: Study hours, attendance, previous marks. | Example: Pass/Fail result. |
Q14. What is the goal of Data Preprocessing?
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Answer:
Data Preprocessing is the process of cleaning and organizing raw data before it is used for training an AI model.
Its main objectives are:
- Remove duplicate records.
- Handle missing values.
- Correct incorrect data.
- Remove outliers and unnecessary information.
- Prepare clean and consistent data for AI models.
Q15. Why is clean data important for Artificial Intelligence?
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Answer:
AI systems learn directly from the data provided to them. If the data contains errors, duplicates, missing values, or incorrect information, the AI model learns incorrect patterns and produces inaccurate predictions.
Therefore, clean and high-quality data is essential for developing reliable AI systems.
Q16. Define Data Interpretation. Why is it important?
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Answer:
Data Interpretation is the process of analyzing processed data to understand its meaning and draw useful conclusions.
It helps organizations identify trends, solve problems, make informed decisions, and improve planning based on factual information.
Q17. Differentiate between Data Processing and Data Interpretation.
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| Data Processing | Data Interpretation |
|---|---|
| Converts raw data into meaningful information. | Analyzes processed information to draw conclusions. |
| Performed mainly by computers or software. | Performed by people to support decision-making. |
| Includes sorting, filtering, and calculations. | Includes analysis, reasoning, and decision-making. |
Q18. Explain the three common types of Data Interpretation.
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Answer:
Data can be interpreted in three common ways:
- Textual Interpretation: Data is explained using sentences and paragraphs.
- Tabular Interpretation: Data is organized into rows and columns for easy comparison.
- Graphical Interpretation: Data is presented using charts and graphs to identify trends and patterns quickly.
Q19. Why is Data Visualization an important step in the AI Project Cycle?
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Answer:
Data Visualization presents information through graphs, charts, and diagrams, making complex data easier to understand. It helps identify trends, relationships, and patterns, allowing developers to choose the most suitable AI model and make better decisions.
Q20. When should a developer use a Line Graph and when should a Pie Chart?
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Answer:
- Line Graph: Used to show changes or trends over time, such as monthly sales or daily temperature.
- Pie Chart: Used to show the proportion or percentage of different categories that together make a whole, such as the percentage of students choosing different subjects.
Q21. [Competency] A restaurant owner notices that sales are decreasing. How can Data Literacy help solve this problem?
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Answer:
The restaurant owner can use Data Literacy by collecting and analyzing data such as customer feedback, sales records, and popular menu items.
After interpreting the data, the owner may find that customers dislike certain dishes or prefer healthier options. Based on these findings, the menu can be improved, unpopular items can be removed, and better services can be introduced to increase customer satisfaction and sales.
Q22. [HOTS] "Raw data is not very useful until it is processed." Justify this statement using the Data Pyramid.
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Answer:
Raw data contains facts without any meaning.
According to the Data Pyramid:
- Data consists of raw facts.
- Information gives meaning to the data.
- Knowledge explains how to use the information.
- Wisdom helps make the correct decision.
For example, the number 38 is only data. When we know it represents 38°C temperature, it becomes information. Understanding that it is very hot becomes knowledge, and deciding to drink more water or stay indoors becomes wisdom.
Q23. [Competency] You find an online dataset for your AI project. What factors will you check before using it?
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Answer:
Before using the dataset, I will check:
- The authenticity and reliability of the source.
- Whether the data is complete and accurate.
- Whether duplicate records or missing values are present.
- Whether the data is relevant to my project.
- Whether I have permission to use the dataset legally and ethically.
Q24. [HOTS] Explain how biased data collection can lead to unethical AI outcomes.
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Answer:
If an AI system is trained using biased or unbalanced data, it learns unfair patterns and makes biased decisions.
For example, if the training data includes information from only one group of people, the AI may produce incorrect or unfair results for people from other groups. This violates ethical principles such as fairness, inclusion, and equality.
Therefore, AI developers should always use balanced, accurate, and representative data.
Q25. [Competency] You are developing an AI model to predict whether a student will pass an examination. Identify the independent and dependent features.
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Answer:
Independent Features (Inputs):
- Study hours
- Attendance percentage
- Previous examination marks
- Assignment completion
Dependent Feature (Output):
- Result (Pass or Fail)
The AI model uses the independent features to predict the dependent feature.
❓ Quick Revision
- Data Literacy is the ability to understand, analyze, interpret, and communicate data.
- The Data Pyramid (DIKW) consists of Data, Information, Knowledge, and Wisdom.
- Data Privacy controls how personal information is used, while Data Security protects data from unauthorized access.
- Primary Data is collected firsthand, whereas Secondary Data is obtained from existing sources.
- Qualitative Data describes qualities, while Quantitative Data represents numerical values.
- Data Preprocessing removes errors, duplicates, and missing values before training an AI model.
- Data Interpretation helps convert processed information into meaningful conclusions.
- Data Visualization uses graphs and charts to identify patterns and trends.
- Independent Features are input variables, while Dependent Features are predicted outputs.
- Clean, accurate, and unbiased data leads to reliable and ethical AI systems.
? Exam Tip
For the CBSE Class 9 Artificial Intelligence examination, remember the DIKW Data Pyramid, Primary vs Secondary Data, Qualitative vs Quantitative Data, Discrete vs Continuous Data, Data Privacy vs Data Security, and the steps of Data Preprocessing. Practice competency-based and HOTS questions, draw comparison tables wherever possible, and underline important keywords to score full marks.