According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module “Identify features of common machine learning types,” the evaluation phase occurs after training and testing a machine learning model. Evaluation metrics are used to measure how well the model performs when applied to data it has not seen before (the validation data).
The machine learning workflow includes the following key steps:
Data Preparation – Importing, cleaning, and transforming data.
Splitting the Data – Dividing it into training and validation (or test) sets.
Model Training – Using the training data to teach the model patterns or relationships.
Model Evaluation – Assessing the trained model using the validation data and evaluation metrics such as accuracy, precision, recall, F1 score, and root mean square error (RMSE).
As stated in the AI-900 content, evaluation metrics are crucial after testing, as they help determine if the model is accurate enough or if it requires retraining with different parameters or algorithms.
A. After you clean the data → incorrect, as metrics cannot be reviewed before training.
B. Before you train a model → incorrect, since the model has not yet learned patterns.
C. Before you choose the type of model → incorrect, as metrics depend on the model’s output.
Therefore, the verified answer is D. After you test a model on the validation data, which is when you review evaluation metrics to determine model performance and readiness for deployment.
