AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a regression model to predict the selling price of houses. After evaluating on a test set, the data scientist wants a metric that measures the average absolute error between predicted and actual prices, expressed in the same units (dollars) as the target variable. Which evaluation metric should the data scientist use?
⚠ Common exam trap
A common mix-up: candidates confuse RMSE with MAE because both are in the same units as the target, but RMSE measures the square root of the average squared error, not the average absolute error, and it gives more weight to large errors.
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
✓
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is the correct metric because it directly measures the average absolute difference between predicted and actual house prices, and its result is expressed in the same unit (dollars) as the target variable. This makes it intuitive for stakeholders to understand the typical prediction error in monetary terms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
R-squared (R²)
Why it's wrong here
R-squared is incorrect because it measures the proportion of variance in the target variable that is explained by the model, expressed as a unitless value between 0 and 1 (or negative for poor fits). It does not quantify the size of prediction errors in dollars; rather, it indicates how well the model explains observed variability relative to a baseline. Therefore it cannot answer an average absolute error question.
When this WOULD be correct
When a data scientist wants to evaluate how well the regression model explains the variability of the target variable, especially to compare models with different scales or to assess goodness-of-fit, R-squared would be the correct metric.
- ✓
Mean Absolute Error (MAE)
Why this is correct
MAE is the correct choice because it calculates the mean of the absolute differences between the predicted and actual dollar values. Because the absolute value is taken before averaging, the error is expressed in the same units as the target variable (dollars), giving the direct average absolute prediction error. It exactly matches the requirement.
- ✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is wrong because, although it is also in dollars, it is the square root of the average of squared errors and therefore reflects the standard deviation of prediction errors rather than the average absolute error. The squaring step causes large errors to dominate the metric, so it can be noticeably higher than the mean absolute error. The question specifically asks for the average absolute error, which is MAE.
When this WOULD be correct
A data scientist wants a metric that penalizes large errors more heavily than small errors, and the metric should be in the same units as the target variable. For example, when predicting house prices, RMSE would be chosen to heavily penalize large prediction errors.
- ✗
Mean Squared Error (MSE)
Why it's wrong here
MSE is incorrect for this requirement because it squares each residual before averaging, producing an error value in squared dollars (dollars²). That unit makes no intuitive sense for a dollar-denominated target, and the squaring also gives disproportionate weight to large errors. It does not represent the average absolute error in the original units.
When this WOULD be correct
A data scientist trains a regression model and wants a metric that penalizes large errors more heavily than small errors, while still being differentiable for optimization. MSE would be the correct choice because it squares the errors, amplifying large deviations.
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.
✓Mean Absolute Error (MAE)Correct answer▾
Why this is correct
MAE is the correct choice because it calculates the mean of the absolute differences between the predicted and actual dollar values. Because the absolute value is taken before averaging, the error is expressed in the same units as the target variable (dollars), giving the direct average absolute prediction error. It exactly matches the requirement.
✗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 average absolute error in dollars. It is unitless and does not express error in the same units as the target variable.
★ When this WOULD be the correct answer
When a data scientist wants to evaluate how well the regression model explains the variability of the target variable, especially to compare models with different scales or to assess goodness-of-fit, R-squared would be the correct metric.
Why candidates choose this
Candidates may confuse R-squared as a measure of prediction error because it is a common regression metric, but they overlook that it does not provide error in the original units of the target variable.
✗Root Mean Squared Error (RMSE)Wrong answer — click to see why▾
Why this is wrong here
RMSE measures the square root of the average squared errors, so it is not in the same units as the target variable (dollars) — it is in dollars, but the squaring emphasizes larger errors, making it not a direct average absolute error.
★ When this WOULD be the correct answer
A data scientist wants a metric that penalizes large errors more heavily than small errors, and the metric should be in the same units as the target variable. For example, when predicting house prices, RMSE would be chosen to heavily penalize large prediction errors.
Why candidates choose this
Candidates may confuse RMSE with MAE because both are in the same units as the target variable, but RMSE is not an average of absolute errors; it is the square root of the average squared errors, which gives more weight to large errors.
✗Mean Squared Error (MSE)Wrong answer — click to see why▾
Why this is wrong here
MSE measures average squared error, so its units are dollars squared, not dollars. The question explicitly requires a metric expressed in the same units (dollars) as the target variable.
★ When this WOULD be the correct answer
A data scientist trains a regression model and wants a metric that penalizes large errors more heavily than small errors, while still being differentiable for optimization. MSE would be the correct choice because it squares the errors, amplifying large deviations.
Why candidates choose this
Candidates may confuse MSE with MAE, thinking both are in original units, or they may not realize that squaring changes the unit of measurement.
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.
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Written by Johnson Ajibi, MSc IT Security
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