- A
Retrain the model using only the most recent three months of data.
Why wrong: Retraining is not possible because original training data is unavailable; also, this action contradicts the constraint.
- B
Increase the decision threshold to a higher value, such as 0.7.
A higher threshold requires stronger evidence for churn, thus reducing false positives.
- C
Collect new labeled data and perform transfer learning from the original model.
Why wrong: This requires new data and retraining, which is not feasible given the constraint of no retraining.
- D
Decrease the decision threshold to a lower value, such as 0.3.
Why wrong: Lowering the threshold would classify more customers as churn, increasing false positives.
Question 56 of 1,000
AI0-001 AI Concepts and Foundations Practice Question
This AI0-001 practice question tests your understanding of ai concepts and foundations. 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 retail company deploys a machine learning model to predict customer churn. The model outputs a probability between 0 and 1, and churn is predicted if probability > 0.5. After deployment, the model has a high false positive rate (many non-churning customers labeled as churn), which leads to unnecessary retention offers and increased costs. The data science team confirms the model was trained on historical data with a balanced class distribution. The business team wants to reduce false positives while maintaining a reasonable true positive rate. However, they cannot retrain the model because the original training data is no longer available. What is the best course of action to reduce false positives?
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
✓
Increase the decision threshold to a higher value, such as 0.7.
Increasing the decision threshold to a higher value, such as 0.7, reduces false positives because the model will only predict churn when it is more confident. Since the model cannot be retrained, adjusting the threshold is the only way to trade off between precision and recall without modifying the model itself.
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.
- ✗
Retrain the model using only the most recent three months of data.
Why it's wrong here
Retraining is not possible because original training data is unavailable; also, this action contradicts the constraint.
- ✓
Increase the decision threshold to a higher value, such as 0.7.
Why this is correct
A higher threshold requires stronger evidence for churn, thus reducing false positives.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Collect new labeled data and perform transfer learning from the original model.
Why it's wrong here
This requires new data and retraining, which is not feasible given the constraint of no retraining.
- ✗
Decrease the decision threshold to a lower value, such as 0.3.
Why it's wrong here
Lowering the threshold would classify more customers as churn, increasing false positives.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that retraining or collecting more data is the only way to fix model performance issues, when in fact threshold tuning is a valid post-deployment technique that does not require retraining.
Detailed technical explanation
How to think about this question
The decision threshold is a hyperparameter that controls the trade-off between sensitivity (true positive rate) and specificity (true negative rate). By increasing the threshold, the model's precision improves because it requires stronger evidence to classify a positive, which is especially useful when the cost of false positives is high. In practice, the optimal threshold can be found using the ROC curve or precision-recall curve to balance business costs.
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?
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the decision threshold to a higher value, such as 0.7. — Increasing the decision threshold to a higher value, such as 0.7, reduces false positives because the model will only predict churn when it is more confident. Since the model cannot be retrained, adjusting the threshold is the only way to trade off between precision and recall without modifying the model itself.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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 →
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Last reviewed: Jul 4, 2026
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