Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist trains a regression model to predict house prices. The model performs poorly on both the training data and the test data, showing high error in both sets. Which concept best describes this situation?
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
Overfitting
Incorrect. Overfitting would show low error on training data but high error on test data, not high error on both.
B
Best answer
Underfitting
Correct. Underfitting means the model is too simplistic to learn the data patterns, causing poor performance on both training and test sets.
C
Distractor review
Data leakage
Incorrect. Data leakage typically causes unrealistically high performance on test data, not poor performance on both sets.
D
Distractor review
Feature scaling
Incorrect. Feature scaling is a preprocessing technique that helps model convergence but is not a cause of uniformly high error.
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: Underfitting — Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high error on both the training and test sets. Overfitting would show low training error but high test error. Data leakage artificially inflates performance. Feature scaling affects training speed but does not directly cause both errors to be high. Thus, underfitting is the correct characterization.
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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