Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist trains a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term 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
Underfitting
Underfitting would result in high error on both training and test sets because the model is too simple to capture the underlying patterns.
B
Best answer
Overfitting
Overfitting means the model has memorized the training data and does not generalize, leading to excellent training metrics but poor test performance.
C
Distractor review
High bias
High bias typically leads to underfitting, where the model is too simple and has high error on both training and test data, not just the test set.
D
Distractor review
High variance
High variance is related to overfitting, but the term 'overfitting' is the direct description of the training-test performance gap. High variance measures sensitivity to training data fluctuations.
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: Overfitting — Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, and fails to generalize to new, unseen data. The low training error and high test error are classic signs. Underfitting would show high error on both. Bias and variance are concepts related to model error, but the specific scenario of training vs. test performance mismatch is overfitting.
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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