- 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.
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.
AI0-001 Machine Learning and Deep Learning Practice Question
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.
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.
Option C is correct because a highly imbalanced dataset often leads the model to predict the majority class for all instances, resulting in high accuracy but zero recall for the minority class. Option A (learning rate) would not cause this behavior; it affects convergence speed. Option B (overfitting) typically reduces generalization but not in this specific pattern. Option D (too large regularization) might cause underfitting but would not necessarily yield zero recall for one class.
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.
- ✗
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
Static NAT maps one inside address to one outside address.
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
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
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.
- →
Machine Learning and Deep Learning — study guide chapter
Learn the concepts, then practise the questions
- →
Machine Learning and Deep Learning practice questions
Targeted practice on this topic area only
- →
All AI0-001 questions
500 questions across all exam domains
- →
CompTIA AI+ AI0-001 study guide
Full concept coverage aligned to exam objectives
- →
AI0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
AI Concepts and Foundations practice questions
Practise AI0-001 questions linked to AI Concepts and Foundations.
Machine Learning and Deep Learning practice questions
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
AI Models and Data Engineering practice questions
Practise AI0-001 questions linked to AI Models and Data Engineering.
AI Implementation and Operations practice questions
Practise AI0-001 questions linked to AI Implementation and Operations.
AI Security, Ethics and Governance practice questions
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
CompTIA A+ hardware practice questions
Practise AI0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
CompTIA A+ networking practice questions
Practise AI0-001 questions linked to CompTIA A+ networking.
CompTIA A+ operating systems practice questions
Practise AI0-001 questions linked to CompTIA A+ operating systems.
CompTIA A+ security practice questions
Practise AI0-001 questions linked to CompTIA A+ security.
CompTIA A+ software troubleshooting questions
Practise AI0-001 questions linked to CompTIA A+ software troubleshooting questions.
CompTIA A+ operational procedures questions
Practise AI0-001 questions linked to CompTIA A+ operational procedures questions.
Practice this exam
Start a free AI0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: The dataset is highly imbalanced, and the model predicts the majority class for all instances. — Option C is correct because a highly imbalanced dataset often leads the model to predict the majority class for all instances, resulting in high accuracy but zero recall for the minority class. Option A (learning rate) would not cause this behavior; it affects convergence speed. Option B (overfitting) typically reduces generalization but not in this specific pattern. Option D (too large regularization) might cause underfitting but would not necessarily yield zero recall for one class.
What should I do if I get this AI0-001 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 AI0-001 NAT questions on configuration and troubleshooting.
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?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 23, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.