AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 180

An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on 10 years of data.

Which metric should the ML engineer use to evaluate the model’s performance?

Answer options

• A. Accuracy
• B. InferenceLatency
• C. Area Under the ROC Curve (AUC)
• D. Root mean square error (RMSE)

Correct answer: D

Explanation

Root mean square error (RMSE) is the most suitable metric for evaluating a model that predicts continuous numeric values, as it measures the average magnitude of the errors between predicted and actual values. Accuracy is inappropriate for regression tasks, while Inference Latency focuses on prediction speed and Area Under the ROC Curve (AUC) is used for binary classification models, making them unsuitable for this scenario.