The answer is that the large difference between MAE and RMSE indicates the model has many small errors and a few large errors. This is because RMSE squares each error before averaging, which heavily penalizes outliers, while MAE treats all errors equally. When interpreting model errors, a significantly higher RMSE than MAE—here $20,000 versus $5,000—reveals that the model’s predictions are mostly accurate, but a handful of predictions are wildly off, inflating the squared term. On the AI-900 exam, this concept tests your understanding of how error metrics behave differently; a common trap is assuming both metrics should be similar, when in fact a large gap signals outlier sensitivity. Remember the memory tip: “MAE is the average miss, RMSE punishes the big kiss”—where “big kiss” stands for large errors that get squared and dominate the RMSE.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 real estate company trains a model to predict house prices. They evaluate it on a test set of 100 houses. The model predictions have a mean absolute error (MAE) of $5,000 and a root mean squared error (RMSE) of $20,000. What does the large difference between MAE and RMSE indicate about the model's errors?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model has many small errors and a few large errors.
The mean absolute error (MAE) of $5,000 and root mean squared error (RMSE) of $20,000 show a large discrepancy because RMSE squares errors before averaging, which heavily penalizes large deviations. Since RMSE is four times larger than MAE, this indicates that while most predictions are close (small errors), there are a few predictions with very large errors that inflate the RMSE. This pattern is classic for a model that performs well on most houses but fails badly on a few outliers.
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 model has many small errors and a few large errors.
Why this is correct
RMSE penalizes large errors heavily; a large gap indicates a few outliers with high error, even if most errors are small.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model consistently overestimates prices.
Why it's wrong here
Bias (consistent over- or underestimation) affects both MAE and RMSE similarly; the gap between them does not indicate direction of error.
✗
The model has a high bias and low variance.
Why it's wrong here
High bias would lead to systematic errors across all predictions, not necessarily a large gap between MAE and RMSE.
✗
The model is perfectly accurate.
Why it's wrong here
If the model were perfectly accurate, both MAE and RMSE would be 0.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a large RMSE always means the model is poor overall, but the question tests the understanding that a large gap between RMSE and MAE specifically reveals the presence of outliers with large errors, not uniform inaccuracy.
Trap categories for this question
Similar concept trap
Bias (consistent over- or underestimation) affects both MAE and RMSE similarly; the gap between them does not indicate direction of error.
Detailed technical explanation
How to think about this question
MAE is the average absolute difference between predicted and actual values, treating all errors equally, while RMSE squares each error before averaging and taking the square root, making it more sensitive to outliers. In practice, when RMSE is significantly larger than MAE, it often indicates the presence of high-leverage outliers—for example, a few luxury homes with prices far outside the training distribution—that dominate the squared error term. This behavior is critical in regression tasks like house price prediction, where a single mispredicted mansion can skew RMSE and mislead model evaluation if not examined alongside MAE.
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: The model has many small errors and a few large errors. — The mean absolute error (MAE) of $5,000 and root mean squared error (RMSE) of $20,000 show a large discrepancy because RMSE squares errors before averaging, which heavily penalizes large deviations. Since RMSE is four times larger than MAE, this indicates that while most predictions are close (small errors), there are a few predictions with very large errors that inflate the RMSE. This pattern is classic for a model that performs well on most houses but fails badly on a few outliers.
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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Question Discussion
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