Question 443 of 1,000

Machine Learning and Deep Learning →hardMultiple ChoiceObjective-mapped

Cost-Sensitive Learning for Imbalanced Data — Fraud Detection

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: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

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 →

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.

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: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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 word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

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

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.

Visual reference

What to study next

Got this wrong? Here's your next step.

Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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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 — Read the scenario before looking for a memorised answer..

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: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.