AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist is training a regression model to predict house prices. The data scientist wants to evaluate the model using a metric that penalizes large prediction errors significantly more than small errors. Which evaluation metric should the data scientist choose?
⚠ Common exam trap
Watch out — candidates often confuse MAE with RMSE, thinking both penalize errors equally, but the squaring operation in RMSE is the key differentiator that makes it penalize large errors disproportionately.
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
✓
Root Mean Squared Error (RMSE)
Root Mean Squared Error (RMSE) is the correct choice because it squares the residuals before averaging, which heavily penalizes large prediction errors (outliers) more than small errors. This aligns with the requirement to penalize large errors significantly more than small ones, as the squaring operation amplifies the impact of larger deviations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mean Absolute Error (MAE)
Why it's wrong here
Mean Absolute Error (MAE) is not the best metric here because it treats every prediction error with equal weight, computing the simple average of absolute differences between predicted and actual values. Unlike RMSE, it does not square the errors, so large outliers are not disproportionately penalized, meaning a model with few large errors but many small ones could look deceptively good under MAE. In house price prediction, where a costly mispricing on a few homes might be especially problematic, MAE would fail to highlight those severe deviations. Therefore, MAE is unsuitable when the goal is to emphasize and penalize larger errors more heavily.
When this WOULD be correct
When the question asks for a metric that is robust to outliers and interprets error in the same unit as the target variable, MAE is correct. For example: 'Which metric should be used to evaluate a regression model when the cost of error is linear and outliers are not a concern?'
- ✓
Root Mean Squared Error (RMSE)
Why this is correct
RMSE squares the errors before averaging and then takes the square root. The squaring step causes larger errors to have a disproportionately higher impact on the metric, making it sensitive to outliers and large deviations.
- ✗
R-squared (R²)
Why it's wrong here
R-squared (R²) measures the proportion of variance in the target variable (house prices) that is explained by the model, not the magnitude or distribution of prediction errors. A model can achieve a high R² yet still have substantial systematic errors, as R² is scale-invariant and does not directly assess how close predictions are to actual values. It also does not apply any squared penalty to individual errors, so it cannot reflect whether larger mispredictions are being punished more. For a regression task focused on error size and the impact of large deviations, R² is a goodness-of-fit statistic, not an error metric, making it an incorrect choice.
When this WOULD be correct
When the question asks for a metric to evaluate the goodness-of-fit of a regression model, specifically how well the independent variables explain the variability of the dependent variable, R-squared would be the correct choice.
- ✗
Mean Absolute Percentage Error (MAPE)
Why it's wrong here
MAPE expresses errors as percentages relative to actual values. While it can be useful, it does not inherently penalize large errors more than small errors and can be undefined or infinite when actual values are zero.
When this WOULD be correct
A data scientist is evaluating a forecasting model for inventory demand where the cost of error is proportional to the percentage deviation from actual demand. MAPE would be appropriate because it measures average absolute percentage error, making it interpretable across different scales.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Root Mean Squared Error (RMSE)Correct answer▾
Why this is correct
RMSE squares the errors before averaging and then takes the square root. The squaring step causes larger errors to have a disproportionately higher impact on the metric, making it sensitive to outliers and large deviations.
✗Mean Absolute Error (MAE)Wrong answer — click to see why▾
Why this is wrong here
MAE treats all errors equally, so it does not penalize large errors more than small errors, which is the requirement in the question.
★ When this WOULD be the correct answer
When the question asks for a metric that is robust to outliers and interprets error in the same unit as the target variable, MAE is correct. For example: 'Which metric should be used to evaluate a regression model when the cost of error is linear and outliers are not a concern?'
Why candidates choose this
Candidates may choose MAE because it is a common regression metric, but they overlook the specific requirement to penalize large errors more heavily.
✗R-squared (R²)Wrong answer — click to see why▾
Why this is wrong here
R-squared measures the proportion of variance explained by the model, not the magnitude of prediction errors. It does not penalize large errors more than small errors, as it is based on squared deviations but is scale-invariant and not directly an error metric.
★ When this WOULD be the correct answer
When the question asks for a metric to evaluate the goodness-of-fit of a regression model, specifically how well the independent variables explain the variability of the dependent variable, R-squared would be the correct choice.
Why candidates choose this
Candidates may confuse R-squared with an error metric or think that because it uses squared terms, it penalizes large errors, but R-squared is a relative measure of fit, not an absolute error metric.
✗Mean Absolute Percentage Error (MAPE)Wrong answer — click to see why▾
Why this is wrong here
MAPE does not penalize large errors significantly more than small errors; it treats errors proportionally to the actual values, and large errors can be masked by small actual values. The question specifically requires a metric that heavily penalizes large errors, which RMSE does via squaring.
★ When this WOULD be the correct answer
A data scientist is evaluating a forecasting model for inventory demand where the cost of error is proportional to the percentage deviation from actual demand. MAPE would be appropriate because it measures average absolute percentage error, making it interpretable across different scales.
Why candidates choose this
Candidates may think MAPE penalizes large errors because it uses percentage, but they overlook that it does not square errors, so large errors are not disproportionately weighted.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Prediction
Prediction is the process of using data and algorithms to forecast future outcomes or identify patterns without explicit programming for each scenario.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
About these practice questions
One of 985 original AI-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.