Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist trains a machine learning model on historical sales data to predict future sales volume. The model achieves 99% accuracy on the training dataset but only 75% accuracy on a separate test dataset. 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
Distractor review
Underfitting
Underfitting would result in low accuracy on both training and test datasets, not high training accuracy and lower test accuracy.
B
Best answer
Overfitting
Overfitting occurs when the model performs very well on training data but poorly on test data due to memorizing training examples instead of learning general patterns.
C
Distractor review
High bias
High bias typically causes underfitting, meaning the model oversimplifies and fails to capture even training data patterns, leading to low training accuracy.
D
Distractor review
High variance
High variance is related to overfitting, but the question asks for the most likely issue. Overfitting is the direct term describing the symptom, while high variance is a cause. In exam context, overfitting is the correct answer.
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 — The large gap between training accuracy (99%) and test accuracy (75%) is a classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data too well, failing to generalize to new, unseen data. Underfitting would show poor performance on both datasets. Bias and variance are related concepts, but the described scenario directly points to high variance leading to overfit.
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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