- A
The model is overfitting the training data
Why wrong: Overfitting refers to the model performing well on training data but poorly on unseen data. The given metrics only describe test set errors; they do not provide information about training performance or overfitting.
- B
The model predictions are consistently biased high
Why wrong: Consistent bias would be detected by examining the mean error (bias), not by comparing MAE and RMSE. The difference between MAE and RMSE does not indicate direction of bias.
- C
The model has some predictions with very large errors
RMSE penalizes large errors more heavily than MAE. A significantly higher RMSE relative to MAE implies that while most errors are moderate, there are a few predictions with extremely large errors (outliers).
- D
The model has high variance due to outliers in training data
Why wrong: High variance (overfitting) is a possible cause, but the given test set metrics alone cannot differentiate between high variance and other causes. The difference in MAE and RMSE directly points to large errors in prediction, not specifically to the source of those errors.
Quick Answer
The answer is that the model has some predictions with very large errors. This is because RMSE squares each error before averaging, which heavily penalizes large deviations, while MAE treats all errors equally; when RMSE is significantly higher than MAE, as in the $8,000 versus $25,000 gap, it signals that a subset of predictions contains extreme outliers that inflate the squared error term. On the Microsoft Azure AI-900 exam, this concept tests your understanding of regression metrics and how to interpret model performance—a common trap is assuming a low MAE alone indicates a good model, when a much larger RMSE reveals hidden large errors. For a memory tip, think of RMSE as the “squeaky wheel” that amplifies big mistakes: if RMSE is three times MAE, expect some predictions to be wildly off.
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 evaluates a regression model that predicts house prices. On the test set, the Mean Absolute Error (MAE) is $8,000 and the Root Mean Squared Error (RMSE) is $25,000. What does the large difference between MAE and RMSE indicate about the model's errors?
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 model has some predictions with very large errors
The large difference between MAE ($8,000) and RMSE ($25,000) indicates that the model has some predictions with very large errors. RMSE squares the errors before averaging, which heavily penalizes large deviations, so a significantly higher RMSE relative to MAE suggests the presence of outliers or extreme prediction errors in the test set.
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 is overfitting the training data
Why it's wrong here
Overfitting refers to the model performing well on training data but poorly on unseen data. The given metrics only describe test set errors; they do not provide information about training performance or overfitting.
- ✗
The model predictions are consistently biased high
Why it's wrong here
Consistent bias would be detected by examining the mean error (bias), not by comparing MAE and RMSE. The difference between MAE and RMSE does not indicate direction of bias.
- ✓
The model has some predictions with very large errors
Why this is correct
RMSE penalizes large errors more heavily than MAE. A significantly higher RMSE relative to MAE implies that while most errors are moderate, there are a few predictions with extremely large errors (outliers).
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model has high variance due to outliers in training data
Why it's wrong here
High variance (overfitting) is a possible cause, but the given test set metrics alone cannot differentiate between high variance and other causes. The difference in MAE and RMSE directly points to large errors in prediction, not specifically to the source of those errors.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the mathematical behavior of RMSE (which amplifies large errors) with concepts like overfitting or bias, rather than recognizing it as a direct indicator of outlier errors in the predictions.
Detailed technical explanation
How to think about this question
MAE computes the average absolute difference between predicted and actual values, treating all errors equally, while RMSE squares the errors before averaging and then takes the square root, making it more sensitive to large errors. In practice, if a model predicts most houses within $8,000 but has a few predictions off by $100,000 or more, the RMSE will be pulled up significantly, revealing the impact of those outliers. This distinction is critical in regression evaluation, especially in Azure Machine Learning when choosing between metrics for model monitoring or automated hyperparameter tuning.
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 model has some predictions with very large errors — The large difference between MAE ($8,000) and RMSE ($25,000) indicates that the model has some predictions with very large errors. RMSE squares the errors before averaging, which heavily penalizes large deviations, so a significantly higher RMSE relative to MAE suggests the presence of outliers or extreme prediction errors in the test set.
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
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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