AIF-C01 Fundamentals of AI and ML Practice Question
A team has built a regression model to predict house prices. The RMSE is 50,000 on the test set. Which action is most appropriate to improve model performance?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the misconception that data preprocessing steps like scaling or outlier removal are universal fixes for high error, when in fact the most appropriate first step for a high RMSE in regression is to improve the feature set to address underfitting.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add more relevant features
Adding more relevant features can provide the model with additional predictive signals, potentially reducing bias and lowering RMSE if the new features have genuine correlation with house prices. Since RMSE is already 50,000, the model may be underfitting due to insufficient input variables, and enriching the feature set is a direct way to capture more variance in the target variable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove outliers from training data
Why it's wrong here
Removing outliers alters the training distribution and can bias predictions, but it does not address the model's systematic error, so RMSE need not fall. It is tempting because outlier removal is genuine data cleaning, and that would be correct when erroneous or impossible values distort training rather than when the model underfits.
- ✗
Apply feature scaling
Why it's wrong here
Feature scaling changes input magnitudes, which leaves tree-based regression predictions unchanged and only affects gradient-based convergence, so test RMSE stays the same. It is tempting because scaling is standard preprocessing, and that would be correct when features span wildly different ranges and the algorithm is distance- or gradient-sensitive.
- ✓
Add more relevant features
Why this is correct
An RMSE of 50,000 indicates underfitting or missing signal, so adding relevant features gives the regression model additional predictive information. This addresses the performance gap more directly than hyperparameter tuning or collecting more rows of the same variables.
- ✗
Use a different evaluation metric
Why it's wrong here
Swapping the evaluation metric re-labels the same errors without changing any prediction, so the model's accuracy is untouched. It is tempting because metric choice genuinely matters, and that would be correct when the metric misaligns with business cost, for example when asymmetric errors need weighting rather than RMSE.
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