Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist is training a regression model to predict house prices. The data scientist wants to evaluate the model using a metric that penalizes large prediction errors significantly more than small errors. Which evaluation metric should the data scientist choose?
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
R-squared (R²)
R-squared indicates the proportion of the variance in the target variable that is explained by the model. It does not directly measure the magnitude of errors or penalize large errors more.
B
Distractor review
Mean Absolute Percentage Error (MAPE)
MAPE expresses errors as percentages relative to actual values. While it can be useful, it does not inherently penalize large errors more than small errors and can be undefined or infinite when actual values are zero.
C
Distractor review
Mean Absolute Error (MAE)
MAE calculates the average absolute difference between predicted and actual values. It does not penalize large errors more than small errors because no squaring is used.
D
Best answer
Root Mean Squared Error (RMSE)
RMSE squares the errors before averaging and then takes the square root. The squaring step causes larger errors to have a disproportionately higher impact on the metric, making it sensitive to outliers and large deviations.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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?
Static NAT maps one inside address to one outside address.
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 averaging, which means larger errors contribute disproportionately more to the final score compared to smaller errors. This makes RMSE a suitable choice when it is important to minimize large errors. In contrast, Mean Absolute Error (MAE) treats all errors equally by averaging absolute differences without squaring. R-squared measures the proportion of variance explained by the model, and Mean Absolute Percentage Error (MAPE) expresses error as a percentage but can be problematic with actual values close to zero.
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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