AI0-001 AI Models and Data Engineering Practice Question
A fraud detection model has high precision but low recall. The cost of false negatives is very high. Which threshold adjustment should be made?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between training-time techniques (like class weights or SMOTE) and post-training threshold tuning, trapping candidates who confuse data-level remedies with decision boundary adjustments.
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
✓
Decrease classification threshold
Decreasing the classification threshold makes the model more sensitive, classifying more instances as positive. This increases recall by catching more true positives, directly addressing the high cost of false negatives, even though precision may drop.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use class weights during training
Why it's wrong here
Class weights influence model training but do not adjust the decision threshold after training.
- ✗
Apply SMOTE to the training data
Why it's wrong here
SMOTE is a data-level technique to handle imbalance during training, not a post-training threshold adjustment.
- ✓
Decrease classification threshold
Why this is correct
Decreasing the threshold increases the number of positive predictions, raising recall and reducing false negatives.
- ✗
Increase classification threshold
Why it's wrong here
Increasing the threshold makes the model more conservative, further lowering recall.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.