- A
The average confidence percentage of classification predictions
Why wrong: Classification confidence is prediction probability — MAE measures the average magnitude of errors in regression predictions.
- B
The average absolute difference between regression model predictions and actual values
MAE = mean of |predicted - actual|. It measures average error magnitude in regression tasks — lower is better.
- C
The proportion of model predictions that deviate from expected values by more than a threshold
Why wrong: Error threshold counts describe precision-recall — MAE is the average absolute error across all predictions.
- D
How much the model's predictions differ from random chance
Why wrong: Comparing to random chance is AUC/lift analysis — MAE measures absolute prediction error magnitude.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
What is the 'mean absolute error' (MAE) metric used to evaluate in machine learning?
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
The average absolute difference between regression model predictions and actual values
Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It measures how close predictions are to the true outcomes, with lower values indicating better model accuracy. In Azure Machine Learning, MAE is commonly used to evaluate regression models like linear regression or decision forests.
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.
- ✗
The average confidence percentage of classification predictions
Why it's wrong here
Classification confidence is prediction probability — MAE measures the average magnitude of errors in regression predictions.
- ✓
The average absolute difference between regression model predictions and actual values
Why this is correct
MAE = mean of |predicted - actual|. It measures average error magnitude in regression tasks — lower is better.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The proportion of model predictions that deviate from expected values by more than a threshold
Why it's wrong here
Error threshold counts describe precision-recall — MAE is the average absolute error across all predictions.
- ✗
How much the model's predictions differ from random chance
Why it's wrong here
Comparing to random chance is AUC/lift analysis — MAE measures absolute prediction error magnitude.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse MAE with classification metrics like accuracy or confidence, or assume it involves thresholds, when in fact MAE is strictly a regression metric measuring average absolute error without any threshold or comparison to random chance.
Detailed technical explanation
How to think about this question
MAE is computed as (1/n) * Σ|y_i - ŷ_i|, where y_i are actual values and ŷ_i are predictions. Unlike Mean Squared Error (MSE), MAE is less sensitive to outliers because it does not square the errors, making it a robust metric for datasets with extreme values. In Azure ML, MAE is automatically reported for regression tasks in automated ML and designer pipelines, and it is scale-dependent, so it should be interpreted relative to the target variable's range.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: The average absolute difference between regression model predictions and actual values — Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It measures how close predictions are to the true outcomes, with lower values indicating better model accuracy. In Azure Machine Learning, MAE is commonly used to evaluate regression models like linear regression or decision forests.
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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Last reviewed: Jun 11, 2026
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