Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A data scientist is training a logistic regression model to predict customer churn using a small dataset with 500 records and 200 features. The model achieves 97% accuracy on the training set but only 65% on a held-out test set, indicating severe overfitting. The data scientist wants to reduce overfitting by automatically eliminating irrelevant features. Which technique should the data scientist apply?
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
Apply L1 regularization (Lasso) to the model
L1 regularization adds a penalty term that can zero out coefficients of less important features, performing feature selection and reducing model complexity to combat overfitting.
B
Distractor review
Apply L2 regularization (Ridge) to the model
L2 regularization penalizes large coefficients but does not eliminate features entirely; it shrinks coefficients but keeps them non-zero, which may not sufficiently reduce complexity when many irrelevant features are present.
C
Distractor review
Use k-fold cross-validation to select the best model
Cross-validation is a technique for evaluating model performance and tuning hyperparameters, but it does not directly reduce overfitting or eliminate features. It would need to be combined with a regularized model.
D
Distractor review
Increase the number of training samples by data augmentation
Data augmentation is common for image or text data, but for structured churn data with 200 features, creating realistic synthetic samples is complex and not a standard direct approach to reduce overfitting.
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: Apply L1 regularization (Lasso) to the model — L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients. This penalty shrinks less important feature coefficients to zero, effectively performing automatic feature selection. This directly addresses the overfitting caused by having too many irrelevant features. L2 regularization (Ridge) also penalizes large coefficients but does not force them to zero, so it does not eliminate features. Cross-validation is a method to assess generalization and tune hyperparameters, not a technique to reduce overfitting by itself. Data augmentation increases dataset size but is not applicable to tabular churn data in this straightforward way.
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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