The answer is Mean Absolute Error (MAE). MAE is the correct choice because it calculates the average absolute difference between each predicted house price and its actual value, directly reporting the error in the same unit (dollars) as the target variable, which makes the result immediately interpretable for stakeholders. In contrast, Root Mean Squared Error (RMSE) squares the differences before averaging and then takes the square root, which also returns dollars but penalizes larger errors more heavily, making it less straightforward for measuring a simple average absolute error. On the Microsoft Azure AI-900 exam, this question tests your ability to match evaluation metrics to business requirements—specifically, choosing MAE when you need an intuitive, unit-aligned measure of typical error. A common trap is selecting RMSE because it is also in the same units, but remember that RMSE emphasizes outliers, while MAE gives a pure average of absolute mistakes. Memory tip: “MAE is the Mean Absolute Error—it’s the ‘average miss’ in the same dollar amount.”
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 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?
Why wrong: R² indicates how well the model explains the variance in the data, but it is a proportion (0–1), not measured in dollars.
B
Mean Absolute Error (MAE)
MAE gives the average absolute error in the same units as the target variable (dollars), which directly answers the requirement.
C
Root Mean Squared Error (RMSE)
Why wrong: RMSE is also in the original unit but gives more weight to large errors; the question specifically asks for average absolute error, which is MAE.
D
Mean Squared Error (MSE)
Why wrong: MSE is the average of squared errors, which is in squared dollars (e.g., dollars²), not the original unit.
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.
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.
✗
R-squared (R²)
Why it's wrong here
R² indicates how well the model explains the variance in the data, but it is a proportion (0–1), not measured in dollars.
✓
Mean Absolute Error (MAE)
Why this is correct
MAE gives the average absolute error in the same units as the target variable (dollars), which directly answers the requirement.
Related concept
Read the scenario before looking for a memorised answer.
✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is also in the original unit but gives more weight to large errors; the question specifically asks for average absolute error, which is MAE.
✗
Mean Squared Error (MSE)
Why it's wrong here
MSE is the average of squared errors, which is in squared dollars (e.g., dollars²), not the original unit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
MAE is calculated as the mean of the absolute residuals (|actual - predicted|), making it robust to outliers compared to MSE or RMSE. In a real-world housing price prediction scenario, MAE is often preferred when the cost of a large error is linear (e.g., a $10,000 error is twice as bad as a $5,000 error), whereas RMSE would disproportionately penalize a few large errors, which might misrepresent typical model performance for a buyer or seller.
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: 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.
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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