Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist trains a regression model to predict daily electricity consumption (in kWh) for a commercial building. The business team needs a metric that heavily penalizes large prediction errors (outliers) more than small errors. Which metric should the data scientist report to best meet this requirement?
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
Mean Absolute Error (MAE)
MAE calculates the average absolute difference between predictions and actual values. It treats all errors equally, so it does not penalize large errors more than small ones.
B
Best answer
Root Mean Squared Error (RMSE)
RMSE squares the errors before averaging, which gives disproportionately higher weight to large errors. This makes it the correct choice when the goal is to penalize outliers more heavily.
C
Distractor review
R-squared
R-squared indicates the proportion of variance in the target variable explained by the model. It does not directly measure error magnitude or penalize large errors differently.
D
Distractor review
Mean Absolute Percentage Error (MAPE)
MAPE expresses errors as percentages of actual values. While it provides relative error, it does not inherently penalize large errors more than small ones.
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: Root Mean Squared Error (RMSE) — Root Mean Squared Error (RMSE) squares the differences between predicted and actual values before taking the square root. This squaring amplifies the impact of large errors, making RMSE more sensitive to outliers compared to Mean Absolute Error (MAE), which treats all errors equally. R-squared measures the proportion of variance explained, not error magnitude. MAPE expresses errors as percentages but does not differentially penalize large errors more than small ones.
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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