- A
The regularization parameter is too large, causing underfitting.
Why wrong: Underfitting reduces performance overall, but it would not specifically cause zero recall for one class while maintaining high accuracy.
- B
The model is overfitting due to too many features.
Why wrong: Overfitting usually gives high training accuracy but poor validation, not a zero-recall pattern on one class.
- C
The learning rate was too high.
Why wrong: A high learning rate may cause divergence or unstable training, but not a zero-recall situation for a specific class.
- D
The dataset is highly imbalanced, and the model predicts the majority class for all instances.
With severe class imbalance, a model can achieve high accuracy by always predicting the majority class, leading to zero recall for the minority class.
Understanding High Accuracy and Low Recall in Imbalanced Datasets
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent transactions. After training a logistic regression model, the accuracy is 99.9%, but the recall for the fraud class is 0%. Which of the following is the MOST likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Quick Answer
The correct choice is that the dataset is highly imbalanced, causing the model to predict the majority class for all instances. This happens because a classifier optimizing for overall accuracy will simply label every transaction as legitimate, achieving 99.9% accuracy while missing all fraudulent cases—hence the 0% recall for the minority class. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of the accuracy paradox in imbalanced datasets, a common trap where high accuracy masks a useless model for rare events. The key insight is that accuracy is misleading when classes are skewed; recall or precision-recall curves are better metrics. A quick memory tip: think of a fire alarm that never rings—99.9% accurate when there’s no fire, but useless when there is one.
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
The dataset is highly imbalanced, and the model predicts the majority class for all instances.
The dataset has a severe class imbalance (99.9% legitimate, 0.1% fraudulent). A logistic regression model that predicts the majority class (legitimate) for every instance will achieve 99.9% accuracy but 0% recall for the fraud class, because it never identifies any positive fraud cases. This is the classic 'accuracy paradox' in imbalanced classification.
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.
- ✗
The regularization parameter is too large, causing underfitting.
Why it's wrong here
Underfitting reduces performance overall, but it would not specifically cause zero recall for one class while maintaining high accuracy.
- ✗
The model is overfitting due to too many features.
Why it's wrong here
Overfitting usually gives high training accuracy but poor validation, not a zero-recall pattern on one class.
- ✗
The learning rate was too high.
Why it's wrong here
A high learning rate may cause divergence or unstable training, but not a zero-recall situation for a specific class.
- ✓
The dataset is highly imbalanced, and the model predicts the majority class for all instances.
Why this is correct
With severe class imbalance, a model can achieve high accuracy by always predicting the majority class, leading to zero recall for the minority class.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the 'accuracy paradox' where candidates mistakenly attribute high accuracy to model quality, ignoring that in imbalanced datasets, a dummy classifier predicting the majority class can achieve the same accuracy, and the trap is to overlook recall or precision for the minority class.
Trap categories for this question
Scenario analysis trap
A high learning rate may cause divergence or unstable training, but not a zero-recall situation for a specific class.
Detailed technical explanation
How to think about this question
In binary classification with extreme class imbalance, logistic regression minimizes cross-entropy loss, which is dominated by the majority class. Without techniques like class weighting, oversampling (SMOTE), or threshold tuning, the model learns to predict the majority class for all inputs because that minimizes overall loss. Recall for the minority class is zero because the decision threshold (typically 0.5) is never crossed for fraud cases, as the predicted probability for fraud remains below that threshold for all instances.
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.
TExam Day Tips
- 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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: The dataset is highly imbalanced, and the model predicts the majority class for all instances. — The dataset has a severe class imbalance (99.9% legitimate, 0.1% fraudulent). A logistic regression model that predicts the majority class (legitimate) for every instance will achieve 99.9% accuracy but 0% recall for the fraud class, because it never identifies any positive fraud cases. This is the classic 'accuracy paradox' in imbalanced classification.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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