The answer is to increase the classification threshold, use a different algorithm that penalizes false positives more, and collect more data for the minority class. Precision measures the proportion of true positives among all positive predictions, so low precision indicates a high number of false positives. Raising the classification threshold makes the model more conservative before labeling a positive, directly reducing false positives, while algorithms like cost-sensitive learning or precision-focused boosting inherently penalize false positives during training. Collecting more data for the minority class helps the model better distinguish the positive class, further reducing mistaken predictions. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of the precision-recall tradeoff—a common trap is confusing precision with accuracy or recall, so remember that precision is about minimizing false alarms, not overall correctness. A useful memory tip: “Precision punishes false positives—raise the bar, pick a strict model, or balance the data.”
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 evaluating a trained binary classification model. The model has high accuracy but the precision is low and recall is high. Which three actions are most appropriate to improve precision? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Collect more training data for the minority class
Increasing the classification threshold reduces false positives, using a different algorithm that penalizes false positives more, and collecting more data for the minority class can all improve precision.
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.
✓
Collect more training data for the minority class
Why this is correct
More minority data helps the model learn better boundaries, often improving precision.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply oversampling to the majority class
Why it's wrong here
Oversampling majority class would increase bias toward majority, likely reducing precision.
Read the scenario before looking for a memorised answer.
✓
Use a different algorithm that penalizes false positives more
Why this is correct
Algorithms with higher penalty on false positives can improve precision.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the classification threshold
Why it's wrong here
Lowering threshold increases recall but decreases precision.
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.
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.
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.
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: Collect more training data for the minority class — Increasing the classification threshold reduces false positives, using a different algorithm that penalizes false positives more, and collecting more data for the minority class can all improve precision.
What should I do if I get this AI0-001 question wrong?
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.
What is the key concept behind this question?
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 →
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. Refer to the exhibit. An AI specialist reviews the model evaluation report for a binary classifier. The specialist wants to improve recall. Which action is most likely effective?
hard
A.Decrease the classification threshold
✓ B.Collect more training data for the minority class
C.Increase the classification threshold
D.Add more features
Why B: Collecting more data for the minority class (the class with lower recall) helps the model learn better representations, often improving recall.
Last reviewed: Jun 23, 2026
Question Discussion
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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.
Question Discussion
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