Artificial Intelligence Governance Professional (AIGP) Practice Exam

Using the same features for training and testing is critical for ensuring the effectiveness of an AI model. This approach helps to maintain consistency in the data that the model sees during both the training and evaluation phases. By using the same set of features, the model can learn patterns and make predictions based on the same variables that will be present in real-world applications. This ensures that the evaluation metrics, such as accuracy or F1 score, are valid and meaningful, reflecting how well the model performs under similar conditions to those it was trained on.

In contrast, ignoring all data types can lead to a significant loss of valuable information that could improve model performance. Relying on a single expert opinion can introduce bias and may not capture the full spectrum of insights necessary for robust model training. Lastly, training with less data is not sufficient as it can lead to overfitting or underfitting, neither of which would yield a reliable model. Properly managing the features used for both training and testing phases is therefore essential for building an effective AI model.