Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist is training a classification model on a dataset with 100 features and only 500 labeled samples. The model achieves 99% accuracy on the training data but only 68% accuracy on a held-out test set, indicating overfitting. Which technique is most appropriate to directly address this problem?
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
Increase the amount of training data by collecting more samples
More data generally helps reduce overfitting, but this option is about 'collecting more samples' which is not always possible and is not a technique applied to the current dataset. The question asks for a direct technique to address overfitting given the existing setup.
B
Best answer
Reduce the number of features used for training
Reducing the number of features (e.g., via feature selection or PCA) decreases model complexity, making it less likely to overfit. This is a standard regularization technique especially useful when features outnumber samples.
C
Distractor review
Increase the complexity of the model by adding more layers
Increasing model complexity (e.g., more layers or neurons) would make overfitting worse because the model becomes more capable of memorizing training data.
D
Distractor review
Train for more epochs
Training for more epochs typically increases the risk of overfitting, as the model will continue to learn noise in the training data. Early stopping is a technique to prevent this, but simply increasing epochs is not appropriate.
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: Reduce the number of features used for training — With a high number of features relative to the number of samples, the model is likely memorizing noise rather than learning general patterns. Reducing the number of features (through feature selection or dimensionality reduction) is a direct approach to combat overfitting by decreasing model complexity. Increasing training data is ideal but not always feasible. Increasing model complexity or using more epochs would likely worsen 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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