Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist is training a regression model to predict house prices using features like square footage, number of bedrooms, and location. After evaluating the model on a test set, the data scientist wants to select a metric that measures the average magnitude of prediction errors in the same units as the target variable (price). Which evaluation metric should the data scientist use?
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
Best answer
Root Mean Squared Error (RMSE)
RMSE measures the average magnitude of prediction errors in the original units, making it suitable for regression.
B
Distractor review
Accuracy
Accuracy is a classification metric that measures the proportion of correct predictions, not suitable for continuous output.
C
Distractor review
F1 Score
F1 score is a classification metric that balances precision and recall, not used for regression.
D
Distractor review
Precision
Precision is a classification metric that measures the proportion of true positives among positive predictions, not applicable to regression.
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) — For regression tasks, common metrics include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. RMSE is the square root of the average of squared differences between predicted and actual values, and it is expressed in the same units as the target variable. MAE also uses the same units but does not penalize large errors as heavily. Accuracy, F1 score, and precision are classification metrics and are not appropriate for regression.
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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