AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 103

An ML engineer needs to use metrics to assess the quality of a time-series forecasting model.

Which metrics apply to this model? (Choose two.)

Answer options

• A. Recall
• B. LogLoss
• C. Root mean square error (RMSE)
• D. InferenceLatency
• E. Average weighted quantile loss (wQL)

Correct answer: C, E

Explanation

Root mean square error (RMSE) is a common metric for measuring the accuracy of continuous predictions, making it suitable for time-series forecasting. Average weighted quantile loss (wQL) is also relevant as it assesses the performance of probabilistic forecasts. Recall and LogLoss are more applicable to classification tasks, while InferenceLatency measures the speed of predictions rather than forecast accuracy.