easyMultiple Choice
PMLE Practice Question: A machine learning engineer wants to monitor…
A machine learning engineer wants to monitor model performance on Vertex AI for a regression model. Which metric is most appropriate to track the average prediction error?
⚠ Common exam trap
Google Cloud often tests the distinction between classification and regression metrics, and the trap here is that candidates mistakenly apply classification metrics like F1, precision, or accuracy to a regression problem, not recognizing that RMSE is the standard for continuous prediction error.
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
✓
RMSE
RMSE (Root Mean Squared Error) is the most appropriate metric for tracking average prediction error in a regression model because it measures the standard deviation of residuals (prediction errors) in the same units as the target variable. On Vertex AI, RMSE is a built-in evaluation metric for regression models, directly quantifying how far predictions deviate from actual values on average.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
F1 score
Why it's wrong here
F1 score combines precision and recall for classification, so it cannot express the magnitude of numeric prediction error in a regression model. It is tempting because F1 is the standard summary metric when classes are imbalanced, and it would be the correct choice for evaluating a binary or multiclass classifier rather than a regression task.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of positive predictions that are truly positive, a classification concept with no meaning for continuous regression output. It is tempting because precision is a core metric when false positives are costly, and it would be the correct choice for monitoring a classifier such as a fraud or spam detector.
- ✗
Accuracy
Why it's wrong here
Accuracy measures the proportion of exactly matching labels, which is undefined for continuous regression targets where predictions rarely equal actuals. It is tempting because accuracy is the default headline metric for classification models, and it would be correct when monitoring a classifier with balanced classes and no severe cost asymmetry.
- ✓
RMSE
Why this is correct
RMSE measures the square root of mean squared prediction error, expressing average deviation in the target's original units. For a regression model, this directly quantifies average prediction error, unlike classification metrics such as accuracy or AUC.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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