Question 1easymultiple choice
Full question →easymultiple choiceObjective-mapped
A data scientist trains a machine learning model to predict housing prices. On the training data, the model achieves an R-squared value of 0.99, but on a separate validation dataset it achieves an R-squared of only 0.65. What is the most likely issue with this model?
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
Overfitting occurs when the model learns the training data too well, capturing noise and making it perform poorly on new, unseen data, as shown by the large gap between training and validation performance.
B
Distractor review
Underfitting
Underfitting would result in poor performance on both training and validation sets, not a large gap where training is near perfect.
C
Distractor review
High bias
High bias typically leads to underfitting, where the model cannot capture the underlying patterns, causing low accuracy on both training and validation data.
D
Distractor review
Insufficient training data
While insufficient data can cause overfitting, the description — very high training performance and much lower validation performance — is a direct symptom of overfitting, not just 'insufficient data' in general.
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 — When a model performs exceptionally well on training data but poorly on unseen validation data, it is a classic sign of overfitting. The model has memorized the training set, including noise and irrelevant patterns, instead of learning generalizable relationships. Underfitting would show poor performance on both sets. A high bias would also cause poor training performance. This scenario clearly indicates 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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