AI Project Cycle

Correct answer: A. The process of helping a machine learn from examples

During training, an AI model receives examples, often with relevant information or labels, and learns patterns that connect inputs with expected outputs. The learned model can later be tested or used on new data. Painting a machine, deleting data, or acting without a goal does not create the learning process called training.

Correct answer: A. With examples and data

In a typical machine-learning process, a model receives examples and data, finds useful patterns or relationships, and uses them to produce predictions or classifications for new inputs. Random guessing alone provides no basis for learning or improvement. Switching off a machine or destroying instructions does not supply training information and therefore cannot begin a meaningful learning process.

Correct answer: B. परिणामों की शुद्धता और विश्वसनीयता की जाँच करना

Testing evaluates how well an AI model performs on suitable data or situations that were not used in the same way for training. It helps measure accuracy, reliability, errors, and sometimes fairness or robustness. Testing is not about shutting down the system, hiding data, or removing its purpose; it is evidence-based evaluation before or during use.

Correct answer: A. Because it provides a clear direction for the solution

A clearly defined problem identifies the need, users, limits, and desired outcome of an AI project. It guides the choice of relevant data, suitable methods, and measures of success. Choosing a problem does not change a machine’s colour, remove all data, or eliminate the need to test and evaluate the final solution.

Correct answer: C. Understanding the problem and defining the goal

An AI project begins by understanding the problem and defining what the project should achieve. This problem-scoping step helps identify the users, required information, constraints, and success criteria. Collecting or preparing data, building a model, and testing its results come after the problem and goal are clear.

Correct answer: B. To check whether the system produces accurate and reliable results

Testing evaluates how well an AI system performs on suitable data that was not used to teach it. It can reveal incorrect predictions, weak performance, or unfair behaviour and guide improvements. Testing is different from training: training develops the model, while testing measures how reliably it works on new cases.