Amazon Web Services MLA-C01 Exam Dumps - Actual Questions Answers

Predicting apartment prices is a regression problem, where the target variable is continuous. AWS documentation states that classification metrics such as accuracy, AUC, and F1 score are not appropriate for regression tasks.

Mean Absolute Error (MAE) measures the average absolute difference between predicted values and actual values. MAE is easy to interpret because it is expressed in the same units as the target variable (for example, dollars), making it especially useful for business-facing problems like price prediction.

AWS best practices recommend MAE for evaluating regression models when understanding average prediction error magnitude is important and when robustness to outliers is desired.

Therefore, Option D is the correct and AWS-aligned answer.

The described behavior—strong performance on training data but poor performance in production—is a classic sign of overfitting. AWS documentation for Amazon SageMaker Object2Vec highlights early stopping as a key regularization technique to prevent models from learning noise in the training data.

The early_stopping_patience hyperparameter controls how many additional epochs the training job will run after the validation loss stops improving. Decreasing this value causes training to stop earlier, reducing the chance that the model overfits to the training dataset.

Option B increases batch size, which may improve training efficiency but does not directly address overfitting. Option C decreases the dropout rate, which actually increases overfitting risk, since dropout is a regularization mechanism. Option D increases epochs, which further worsens overfitting.

AWS best practices emphasize early stopping combined with validation metrics as one of the most effective ways to maintain generalization performance in neural embedding models such as Object2Vec.

Therefore, Option A is the correct and AWS-aligned solution.

The scenario describes a classic overfitting problem: the XGBoost model performs well on the training dataset but poorly on the test hold-out dataset. According to AWS Machine Learning and XGBoost documentation, overfitting occurs when a model is too complex and learns noise and patterns specific to the training data rather than generalizable relationships.

One effective way to address overfitting is to reduce model complexity. Option B, reducing the number of features, simplifies the hypothesis space and lowers the risk of fitting spurious correlations. Feature reduction is a recommended best practice when the model shows a large generalization gap between training and test error.

Another effective method is to increase regularization. Option D, increasing the L2 regularization parameter (lambda in XGBoost), penalizes large weights and discourages overly complex trees. AWS documentation explicitly notes that L2 regularization helps improve generalization by smoothing model parameters and reducing variance.

Option A would worsen overfitting by increasing complexity. Option C is incorrect because reducing the number of training samples generally increases overfitting risk. Option E would decrease regularization strength and further degrade test performance.

Therefore, reducing feature complexity and increasing L2 regularization are the correct changes.