Artificial Intelligence

Data features

Q: What qualities should good-quality data have for use in Artificial Intelligence?

Accurate, clean, and relevant Option A is correct because an AI system learns patterns from the data it receives. Accurate data reflects the real situation, clean data has fewer errors, duplicates, or unnecessary entries, and relevant data is connected to the task being solved. These qualities help the model produce dependable results. Incomplete or inconsistent data can confuse learning, outdated data may not represent current conditions, and intentionally biased data can lead to unfair or misleading predictions.

Data Features introduces Class 9 students to the properties that make data useful in Artificial Intelligence. As part of Data Literacy, students explore attributes such as data type, size, range, format, accuracy, completeness, and consistency, and learn how these features affect interpretation and model outcomes. They connect tables and simple datasets with real-world questions, identify patterns or irregularities, and understand why well-described, reliable data supports fairer and more meaningful AI solutions.

Medium · Level 1 · 2 questions

Practice questions

01What qualities should good-quality data have for use in Artificial Intelligence?

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Answer and explanation

Correct answer: A. Accurate, clean, and relevant

Option A is correct because an AI system learns patterns from the data it receives. Accurate data reflects the real situation, clean data has fewer errors, duplicates, or unnecessary entries, and relevant data is connected to the task being solved. These qualities help the model produce dependable results. Incomplete or inconsistent data can confuse learning, outdated data may not represent current conditions, and intentionally biased data can lead to unfair or misleading predictions.

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02What is the main use of a feature in Artificial Intelligence?

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Answer and explanation

Correct answer: A. डेटा में पैटर्न पहचानने और निर्णय या पूर्वानुमान करने में सहायता करना

A feature is a measurable or descriptive property used to represent an input, such as colour, size, word frequency, or an edge in an image. The model examines features to find patterns and support classification, prediction, or another decision. Turning off a computer, deleting data, or stopping training is an operation, not the purpose of a feature.