Microsoft Azure AI Fundamentals (AI-900) Practice Exam

The 'Predicted vs. True' chart is an essential tool for assessing a model's performance, particularly in the context of evaluating how well a model's predictions align with the actual outcomes. This type of chart visually compares the predicted values generated by the model against the true values from the dataset.

When a model is trained, it is expected to predict outcomes based on certain input data. The effectiveness of these predictions can be analyzed using the 'Predicted vs. True' chart. A close alignment of predicted values with true values indicates high prediction accuracy and suggests that the model has a good fit for the underlying data. In contrast, significant deviations between these values may reveal weaknesses in the model's ability to generalize or capture the underlying patterns, illustrating areas where it may underperform.

The other options focus on aspects not directly related to the measurement of prediction accuracy. Clarity of data presentation is concerned with how data is visualized rather than model assessment. Data preprocessing effectiveness relates to the steps needed to prepare data for modeling, which is a separate concern. Feature engineering efficiency deals with how well features are designed and utilized in the model, not directly measuring predictions against actual outcomes. Therefore, the focus of the 'Predicted vs. True' chart clearly