Model Evaluation — AWS AI Practitioner (AIF-C01) Practice Questions
Model evaluation is the process of measuring how well a trained model performs against a defined objective using metrics appropriate to the problem type. For classification tasks, the AIF-C01 exam emphasizes metrics such as accuracy, precision, recall, F1 score, and AUC-ROC, while regression tasks use metrics like RMSE and MAE. Candidates must understand how to interpret these metrics and use tools such as Amazon SageMaker Model Monitor to track model performance after deployment.
Free questions on model evaluation
In machine learning, what does "overfitting" refer to?
Free question · easy · full answer + explanation
Which metric is most appropriate for evaluating a multi-class classification model?
Free question · medium · full answer + explanation
What is the purpose of cross-validation in machine learning?
Free question · medium · full answer + explanation
More model evaluation questions in the full bank
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- In the Amazon Bedrock Playground, how would you test cross-lingual performance of a model before deploying a multi-language application? Unlock answer & explanation →