The correct answer is that an MAE of $5,000 means the model’s predictions are, on average, $5,000 away from the actual house prices. This is because Mean Absolute Error directly calculates the average of the absolute differences between each predicted value and its corresponding true value, so the metric tells you the typical magnitude of error regardless of direction. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to interpret regression metrics rather than perform calculations—a common trap is confusing MAE with Mean Squared Error (MSE), which penalizes larger errors more heavily. Remember that MAE is linear and intuitive: it answers “on average, how far off am I?” in the same units as the target variable. A simple memory tip is to think of MAE as the “mean absolute error” where “absolute” means you ignore whether the prediction is too high or too low, focusing only on the distance.
AI-900 MAE 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: mAE 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 trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
On average, the model's predictions are $5,000 away from the actual prices.
Option A is correct because Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values. An MAE of $5,000 means that, on average, each prediction deviates from the true house price by $5,000. This is a standard interpretation of MAE in regression metrics.
Key principle: MAE 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.
✓
On average, the model's predictions are $5,000 away from the actual prices.
Why this is correct
Correct. Mean Absolute Error (MAE) is the average absolute difference between predicted and actual values.
Clue confirmation
The clue word "best" in the question point toward this answer.
Accuracy is a classification metric, not used for regression. MAE does not indicate a percentage of correct predictions.
✗
The model's predictions are within $5,000 of the actual prices for 50% of the houses.
Why it's wrong here
This describes a quantile error (e.g., median absolute error), not the mean absolute error.
✗
The square root of the average squared error is $5,000.
Why it's wrong here
This describes Root Mean Squared Error (RMSE), which penalizes larger errors more heavily. MAE does not involve squaring.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse MAE with RMSE or misinterpret it as a percentage accuracy or percentile bound, leading them to select options B, C, or D.
Detailed technical explanation
How to think about this question
MAE is computed as the mean of absolute residuals: (1/n) * Σ|y_i - ŷ_i|. Unlike RMSE, MAE is less sensitive to outliers because it does not square errors. In real-world scenarios like house price prediction, MAE provides a straightforward interpretation of typical prediction error in the same unit as the target variable (dollars), making it useful for business stakeholders.
KKey Concepts to Remember
MAE is a regression evaluation metric.
MAE calculates the average absolute difference between predictions and actual values.
MAE is expressed in the same units as the target variable.
MAE gives equal weight to all errors, regardless of their magnitude.
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
MAE is a regression evaluation metric.
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. MAE is a regression evaluation metric. 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 — MAE is a regression evaluation metric..
What is the correct answer to this question?
The correct answer is: On average, the model's predictions are $5,000 away from the actual prices. — Option A is correct because Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values. An MAE of $5,000 means that, on average, each prediction deviates from the true house price by $5,000. This is a standard interpretation of MAE in regression metrics.
What should I do if I get this AI-900 question wrong?
Review mAE is a regression evaluation metric., then practise related AI-900 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
MAE is a regression evaluation metric.
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