The answer is Root Mean Squared Error (RMSE) because it is the regression evaluation metric that keeps RMSE in the same units as the target variable, making it directly interpretable for tasks like house price prediction. RMSE calculates the square root of the average of squared differences between predicted and actual values, which cancels out the squaring effect and returns the error measure to the original unit—dollars in this case. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to match evaluation metrics to specific business requirements, often contrasting RMSE with Mean Absolute Error (MAE) or R-squared. A common trap is choosing Mean Squared Error (MSE), which gives errors in squared units (e.g., dollars squared), making it less intuitive for stakeholders. Remember the memory tip: “RMSE returns to the same unit—just take the root to stay in the same suit.”
AI-900 RMSE is a regression evaluation metric. Practice Question
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: rMSE is a regression evaluation metric.. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is training a regression model to predict house prices using features like square footage, number of bedrooms, and location. After evaluating the model on a test set, the data scientist wants to select a metric that measures the average magnitude of prediction errors in the same units as the target variable (price). Which evaluation metric should the data scientist use?
RMSE measures the average magnitude of prediction errors in the original units, making it suitable for regression.
B
Accuracy
Why wrong: Accuracy is a classification metric that measures the proportion of correct predictions, not suitable for continuous output.
C
F1 Score
Why wrong: F1 score is a classification metric that balances precision and recall, not used for regression.
D
Precision
Why wrong: Precision is a classification metric that measures the proportion of true positives among positive predictions, not applicable to regression.
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 metric because it measures the average magnitude of prediction errors in the same units as the target variable (price). RMSE is computed as the square root of the average squared differences between predicted and actual values, which brings the error metric back to the original unit (e.g., dollars), making it directly interpretable for regression tasks like house price prediction.
Key principle: RMSE is a regression evaluation metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Root Mean Squared Error (RMSE)
Why this is correct
RMSE measures the average magnitude of prediction errors in the original units, making it suitable for regression.
Accuracy is a classification metric that measures the proportion of correct predictions, not suitable for continuous output.
✗
F1 Score
Why it's wrong here
F1 score is a classification metric that balances precision and recall, not used for regression.
✗
Precision
Why it's wrong here
Precision is a classification metric that measures the proportion of true positives among positive predictions, not applicable to regression.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse regression metrics with classification metrics, mistakenly selecting Accuracy or F1 Score because they are familiar from other contexts, without recognizing that the question explicitly asks for a metric measuring error magnitude in the same units as the target variable, which only RMSE (or MAE) satisfies.
Trap categories for this question
Command / output trap
Accuracy is a classification metric that measures the proportion of correct predictions, not suitable for continuous output.
Detailed technical explanation
How to think about this question
RMSE penalizes larger errors more heavily than smaller ones due to the squaring step before averaging, making it sensitive to outliers—a key consideration when house prices may have extreme values (e.g., luxury mansions). In Azure Machine Learning, RMSE is automatically computed for regression models during automated ML runs and can be selected as the primary metric for model comparison. A subtle behavior: RMSE is always in the same unit as the target variable, but unlike Mean Absolute Error (MAE), it is not robust to outliers, which can mislead interpretation if the dataset contains significant price anomalies.
KKey Concepts to Remember
RMSE is a regression evaluation metric.
RMSE measures the average magnitude of prediction errors.
RMSE is expressed in the same units as the target variable.
Lower RMSE values indicate better model performance.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
RMSE is a regression evaluation metric.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — RMSE is a regression evaluation metric..
What is the correct answer to this question?
The correct answer is: Root Mean Squared Error (RMSE) — Root Mean Squared Error (RMSE) is the correct metric because it measures the average magnitude of prediction errors in the same units as the target variable (price). RMSE is computed as the square root of the average squared differences between predicted and actual values, which brings the error metric back to the original unit (e.g., dollars), making it directly interpretable for regression tasks like house price prediction.
What should I do if I get this AI-900 question wrong?
Review rMSE is a regression evaluation metric., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
RMSE is a regression evaluation metric.
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