Question 220 of 500

Machine Learning and Deep Learning →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to use cost-sensitive learning by assigning a higher misclassification cost to the fraud class. This technique directly modifies the loss function—typically standard cross-entropy—to penalize false negatives more heavily than false positives, which forces the neural network to prioritize catching the rare fraudulent transactions without requiring new data or architectural changes. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of how cost-sensitive learning for imbalanced fraud detection differs from resampling methods; a common trap is assuming random undersampling alone solves class imbalance, but it often increases false positives and misses fraud. Remember the mnemonic “Cost Cuts False Negatives”—when business requirements demand high recall with controlled precision, adjusting misclassification costs is the most resource-efficient fix.

AI0-001 Machine Learning and Deep Learning Practice Question

This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial institution is developing a fraud detection model using historical transaction data. The dataset contains over 10 million records, but only 0.01% of transactions are fraudulent. The current model uses a neural network trained with standard cross-entropy loss, and the team applies random undersampling of the majority class to create a balanced training set. However, the model still produces a high number of false positives (legitimate transactions flagged as fraud) and misses approximately 30% of actual fraud cases. The business requires that at least 95% of frauds be caught, and the false positive rate must be below 1% to avoid overwhelming fraud analysts. The team has limited resources to collect additional data and cannot change the model architecture significantly. Which approach should the team take to best meet the business requirements?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

Question 1hardmultiple choice

Full question →

A
Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.
This directly penalizes false negatives more, encouraging the model to catch more frauds while maintaining a low false positive rate through tuning.
B
Apply feature selection to remove noisy predictors and then retrain the current model.
Why wrong: Feature selection may help but does not directly address the class imbalance or the specific performance targets for recall and false positive rate.
C
Switch to an anomaly detection algorithm such as Isolation Forest or One-Class SVM.
Why wrong: Anomaly detection typically assumes outliers are rare and distinct, but transaction fraud can be very similar to legitimate behavior, leading to high false positive rates.
D
Collect more transaction data, especially fraudulent examples, to naturally balance the classes.
Why wrong: Collecting more data is resource-intensive and may not be feasible; oversampling existing fraud data could cause overfitting.

Full breakdown with real-world context →

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

✓

Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.

Cost-sensitive learning adjusts the loss function to penalize false negatives more heavily, directly addressing the need to catch more frauds while controlling false positives. Collecting more data is impractical and may not resolve the imbalance. Anomaly detection models treat fraud as outliers but often have high false positive rates in this context. Feature selection does not inherently solve the imbalance or performance metric trade-off.

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.

✓
Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.
Why this is correct
This directly penalizes false negatives more, encouraging the model to catch more frauds while maintaining a low false positive rate through tuning.
Clue confirmation
The clue words "best", "least" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Apply feature selection to remove noisy predictors and then retrain the current model.
Why it's wrong here
Feature selection may help but does not directly address the class imbalance or the specific performance targets for recall and false positive rate.
✗
Switch to an anomaly detection algorithm such as Isolation Forest or One-Class SVM.
Why it's wrong here
Anomaly detection typically assumes outliers are rare and distinct, but transaction fraud can be very similar to legitimate behavior, leading to high false positive rates.
✗
Collect more transaction data, especially fraudulent examples, to naturally balance the classes.
Why it's wrong here
Collecting more data is resource-intensive and may not be feasible; oversampling existing fraud data could cause overfitting.

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.

Trap categories for this question

Similar concept trap
Anomaly detection typically assumes outliers are rare and distinct, but transaction fraud can be very similar to legitimate behavior, leading to high false positive rates.

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 AI0-001 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this AI0-001 question test?

Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class. — Cost-sensitive learning adjusts the loss function to penalize false negatives more heavily, directly addressing the need to catch more frauds while controlling false positives. Collecting more data is impractical and may not resolve the imbalance. Anomaly detection models treat fraud as outliers but often have high false positive rates in this context. Feature selection does not inherently solve the imbalance or performance metric trade-off.

What should I do if I get this AI0-001 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "best", "least". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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