Test: Revisiting AI Project Cycle - Class 10 MCQ

The AI Project Cycle begins with Problem Scoping, where the specific problem to solve is defined to set clear goals for the project.

Data Collection involves acquiring trustworthy data and visually representing it to find patterns necessary for model building.

Model Selection is where patterns in the data are analyzed and the best-fitting AI model is chosen for the project.

Model Testing is the process of evaluating selected models to determine which performs best for the project’s goals.

Model Development involves building the algorithm around the chosen model and refining it with new data to improve performance.

Deployment integrates the AI solution into real-world environments to provide practical value and impact.

Statistical Data focuses on collecting and analyzing large amounts of data to extract meaningful insights for AI applications.

Computer Vision enables machines to interpret visual data through steps like image acquisition, screening, and identification.

NLP focuses on enabling computers to read, understand, and interpret human languages naturally.

Emotional Intelligence Analysis is unrelated to the AI Project Cycle stages, which focus on data and model processes.