- A
Increase the regularization parameter to prevent overfitting.
Why wrong: Regularization addresses overfitting, not imbalance; it may not help recall if the model already underfits the minority class.
- B
Collect more data, especially of churned customers.
Why wrong: While more data can help, it is often impractical and time-consuming; oversampling is a more immediate and effective solution.
- C
Oversample the minority class using SMOTE to create synthetic churn examples.
SMOTE generates synthetic instances of the minority class, balancing the dataset and improving recall without losing information.
- D
Undersample the majority class to match the minority class size.
Why wrong: Undersampling discards many non-churn records, losing valuable information and potentially reducing overall performance.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A telecom company uses Einstein Discovery to predict customer churn. The training dataset contains 100,000 records, but only 5% represent churned customers. The model achieves 95% accuracy on a holdout test set, but the recall for churn is only 20%. The business wants to proactively retain at-risk customers, so they need to identify as many churners as possible. What action should the data scientist take to improve churn recall?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Oversample the minority class using SMOTE to create synthetic churn examples.
Class imbalance causes the model to favor the majority class. Oversampling the minority class (e.g., using SMOTE) balances the dataset, helping the model learn churn patterns better and improve recall.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the regularization parameter to prevent overfitting.
Why it's wrong here
Regularization addresses overfitting, not imbalance; it may not help recall if the model already underfits the minority class.
- ✗
Collect more data, especially of churned customers.
Why it's wrong here
While more data can help, it is often impractical and time-consuming; oversampling is a more immediate and effective solution.
- ✓
Oversample the minority class using SMOTE to create synthetic churn examples.
Why this is correct
SMOTE generates synthetic instances of the minority class, balancing the dataset and improving recall without losing information.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Undersample the majority class to match the minority class size.
Why it's wrong here
Undersampling discards many non-churn records, losing valuable information and potentially reducing overall performance.
Common exam traps
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.
Detailed technical explanation
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.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Data for AI — This question tests Data for AI — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Oversample the minority class using SMOTE to create synthetic churn examples. — Class imbalance causes the model to favor the majority class. Oversampling the minority class (e.g., using SMOTE) balances the dataset, helping the model learn churn patterns better and improve recall.
What should I do if I get this AI Associate question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI Associate NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 2026
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