Quick Answer
The answer is recall (true positive rate). Recall is the correct evaluation metric for imbalanced classification because it measures the model’s ability to correctly identify all actual positive cases—in this scenario, the churners—regardless of how many non-churners are misclassified. When a dataset has 80% non-churn and 20% churn, a model can achieve 95% accuracy by simply predicting the majority class, but that fails the business goal of catching churners. On the CompTIA AI+ AI0-001 exam, this question tests your understanding that accuracy is misleading for imbalanced data, and that recall prioritizes sensitivity to the minority class. A common trap is choosing F1-score, which balances precision and recall, but recall is more directly needed when the cost of missing churners is high. Memory tip: “Recall the rare ones”—if you need to catch every positive, recall is your go-to metric.
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 data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 80% non-churn and 20% churn. The model achieves 95% accuracy but fails to identify churners correctly. Which metric should the scientist focus on to evaluate model performance properly?
Precision
Why wrong: Precision measures accuracy of positive predictions, but the priority is catching actual churners.
F1-score
Why wrong: F1-score is a harmonic mean of precision and recall, but recall alone is more appropriate here.
Recall (TPR)
Recall focuses on identifying positive cases, which is the main objective.
Specificity
Why wrong: Specificity measures true negative rate, not relevant for identifying churners.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall (TPR)
Option A is correct because recall (true positive rate) measures the ability to find positive (churn) cases, which is the goal in an imbalanced dataset. Option B, precision, is important but less critical when the cost of missing churners is high. Option C, F1-score, balances precision and recall but recall is more directly needed. Option D, specificity, measures true negative rate, not relevant for catching churners.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Precision
Why it's wrong here
Precision measures accuracy of positive predictions, but the priority is catching actual churners.
F1-score
Why it's wrong here
F1-score is a harmonic mean of precision and recall, but recall alone is more appropriate here.
Recall (TPR)
Why this is correct
Recall focuses on identifying positive cases, which is the main objective.
Related concept
Read the scenario before looking for a memorised answer.
Specificity
Why it's wrong here
Specificity measures true negative rate, not relevant for identifying churners.
Common exam traps
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.
Detailed technical explanation
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise AI0-001 questions linked to AI Concepts and Foundations.
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
Practise AI0-001 questions linked to AI Models and Data Engineering.
Practise AI0-001 questions linked to AI Implementation and Operations.
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
Practise AI0-001 questions linked to CompTIA A+ hardware.
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
Practise AI0-001 questions linked to CompTIA A+ networking.
Practise AI0-001 questions linked to CompTIA A+ operating systems.
Practise AI0-001 questions linked to CompTIA A+ security.
Practise AI0-001 questions linked to CompTIA A+ software troubleshooting questions.
Practise AI0-001 questions linked to CompTIA A+ operational procedures questions.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Recall (TPR) — Option A is correct because recall (true positive rate) measures the ability to find positive (churn) cases, which is the goal in an imbalanced dataset. Option B, precision, is important but less critical when the cost of missing churners is high. Option C, F1-score, balances precision and recall but recall is more directly needed. Option D, specificity, measures true negative rate, not relevant for catching churners.
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.
Read the scenario before looking for a memorised answer.
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 →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with 99% legitimate and 1% fraudulent. Which evaluation metric should be prioritized to assess model performance?
easyWhy B: Option A (Accuracy) is misleading because a model that always predicts 'legitimate' would achieve 99% accuracy but fail to detect fraud. Option C (Mean Squared Error) is for regression, not classification. Option D (Log Loss) can be used but is less interpretable for imbalanced data. Option B (F1-score) balances precision and recall, making it ideal for imbalanced datasets.
Variation 2. A data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?
easyWhy A: In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.
Keep practising
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.