The answer is Root Mean Squared Error (RMSE). RMSE is the correct additional metric because it squares each residual before averaging, which heavily penalizes large errors like a $500,000 underprediction, whereas Mean Absolute Error (MAE) treats all errors equally. In the context of the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how different regression metrics handle outliers and business impact—a common trap is assuming MAE is always sufficient, but when the cost of large mistakes outweighs average performance, RMSE is the better choice. For the exam, remember that RMSE amplifies big errors due to squaring, making it sensitive to outliers, while MAE gives a linear penalty. A quick memory tip: "Square the big ones" to recall that RMSE punishes large deviations more harshly than MAE.
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 science team trains a regression model to predict house prices. They evaluate the model using Mean Absolute Error (MAE). After deployment, they notice that the model occasionally produces large errors (e.g., underpredicting a luxury home by $500,000) while most predictions are within $20,000. The business is more concerned about the impact of these large errors than the average small error. Which additional metric should the team use to better capture the penalty for large errors?
RMSE squares errors, so large errors contribute much more to the metric, reflecting their negative impact.
B
R-squared
Why wrong: R-squared measures goodness-of-fit (variance explained), not the size of errors, and does not penalize outliers more.
C
Mean Absolute Percentage Error (MAPE)
Why wrong: MAPE expresses errors as percentages, but it still treats all errors equally; large dollar errors may have small percentage errors if the house is expensive.
D
F1 score
Why wrong: F1 score is used for classification models (precision/recall), not 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 additional metric because it squares the residuals before averaging, which heavily penalizes large errors like the $500,000 underprediction. Unlike MAE, which treats all errors equally, RMSE amplifies the impact of outliers, making it a better fit for a business that cares more about catastrophic failures than typical small errors. This aligns with the need to capture the penalty for large deviations in regression model evaluation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 squares errors, so large errors contribute much more to the metric, reflecting their negative impact.
Related concept
Read the scenario before looking for a memorised answer.
✗
R-squared
Why it's wrong here
R-squared measures goodness-of-fit (variance explained), not the size of errors, and does not penalize outliers more.
✗
Mean Absolute Percentage Error (MAPE)
Why it's wrong here
MAPE expresses errors as percentages, but it still treats all errors equally; large dollar errors may have small percentage errors if the house is expensive.
✗
F1 score
Why it's wrong here
F1 score is used for classification models (precision/recall), not regression.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose MAE or MAPE because they seem intuitive for 'average error,' but they fail to recognize that RMSE's squared term is specifically designed to penalize large outliers, which is the exact business concern described.
Detailed technical explanation
How to think about this question
Under the hood, RMSE computes the square root of the average of squared differences between predicted and actual values, which gives it a unit that matches the target variable (e.g., dollars). This squaring step means that an error of $500,000 contributes 250,000,000 to the sum of squares, whereas an error of $20,000 contributes only 400,000,000—a 625x difference in squared terms, even though the raw error is only 25x larger. In real-world scenarios like luxury home pricing, RMSE ensures that the model is tuned to avoid rare but costly mispredictions, which MAE would underweight.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 — Read the scenario before looking for a memorised answer..
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 additional metric because it squares the residuals before averaging, which heavily penalizes large errors like the $500,000 underprediction. Unlike MAE, which treats all errors equally, RMSE amplifies the impact of outliers, making it a better fit for a business that cares more about catastrophic failures than typical small errors. This aligns with the need to capture the penalty for large deviations in regression model evaluation.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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