Artificial Intelligence — CBSE Class 10 board question

Significance of AI Project Cycle:
The AI Project Cycle provides a structured framework to develop AI projects efficiently. It guides developers through six stages — Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation, and Deployment — ensuring clarity at each step. It helps set clear goals, organise data, build accurate models, and deliver real-world solutions systematically.

Data Acquisition vs. Data Exploration:

| Aspect | Data Acquisition | Data Exploration |
|---|---|---|
| Meaning | Collecting data from various reliable and authentic sources | Analysing collected data to identify patterns and trends |
| Purpose | To build the data foundation for the project | To interpret and understand the data visually |
| Method | Gathering raw data | Representing data via graphs, charts, databases, flow charts, maps, etc. |

Thus, Data Acquisition focuses on collecting data, whereas Data Exploration focuses on understanding the collected data through visual representations and pattern recognition.

Source: AI Project Cycle, Section 1.1

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• Examiners expect the significance of the AI Project Cycle to be briefly stated (1–2 marks), followed by a clear distinction between Data Acquisition and Data Exploration (2 marks).
• Using a comparison table is effective for "difference" questions and saves words while earning full marks.
• Key terms to include: "reliable sources" for acquisition; "graphs, databases, flow charts" for exploration — these are directly from the textbook.
• Avoid writing lengthy paragraphs; crisp points/table are preferred.