Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
The model is overfitting the training data
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
Distractor review
The model predictions are consistently biased high
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
Best answer
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
Distractor review
The model has high variance due to outliers in training data
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 trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 — RMSE gives more weight to large errors because it squares the errors before averaging, while MAE treats all errors linearly. A large difference between RMSE and MAE (RMSE is much larger) indicates that the model's prediction errors include some very large outliers. This is a key insight for regression evaluation. The other options are not directly supported by the given information.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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