Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves an R-squared of 0.95 on the test set. However, when deployed to predict prices in a new city with different property characteristics, the predictions are very inaccurate. Which concept best explains this poor performance?
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
Overfitting
The model performs well on the original test set but fails on data from a different distribution (new city), which is a classic sign of overfitting.
B
Distractor review
Underfitting
Underfitting would cause poor performance on both training and test sets, which is not the case here.
C
Distractor review
High bias
High bias is associated with underfitting, where the model is too simple to capture patterns. This model shows high variance (overfitting).
D
Distractor review
Data drift
Data drift refers to a gradual change in data distribution over time, but the issue here is immediate poor generalization to a different city, not a temporal change.
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: Overfitting — Overfitting occurs when a model learns training data too well, including noise, and fails to generalize to new, unseen data. The high R-squared on the test set but poor performance on a different city indicates the model is overfitted to the original data distribution. Underfitting would perform poorly even on training data. High bias is another term for underfitting. Data drift refers to a gradual change in data distribution over time, not a sudden shift to a new city.
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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