easyMultiple Choice
AIF-C01 Practice Question: A binary classification model outputs…
A binary classification model outputs probabilities. The default threshold of 0.5 results in high precision but low recall. Which action would likely increase recall while maintaining acceptable precision?
⚠ Common exam trap
AWS often tests the misconception that changing the evaluation metric (like F1 score) or resampling the data (like oversampling) directly adjusts the model's output threshold, when in fact only threshold tuning changes the classification boundary after training.
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 the threshold to 0.3
Decreasing the threshold to 0.3 makes the model classify more instances as positive, which increases recall (more true positives captured) but may also increase false positives. The goal is to shift the precision-recall trade-off toward higher recall while keeping precision at an acceptable level, which is directly achieved by lowering the decision threshold.
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 F1 score instead of accuracy
Why it's wrong here
F1 score is an evaluation metric, not a decision threshold, so it cannot change which predictions the model labels positive. It is tempting because it balances precision and recall, but it would be the right choice only for comparing model performance, not for trading recall against precision at inference.
- ✓
Decrease the threshold to 0.3
Why this is correct
Lowering the threshold to 0.3 classifies more cases as positive, so additional true positives are captured and recall rises. Precision may dip slightly, but the stem accepts this provided precision stays acceptable, which a modest 0.3 threshold typically preserves.
- ✗
Apply oversampling to the minority class
Why it's wrong here
Oversampling the minority class changes the training distribution, requiring retraining, and does not directly adjust the decision threshold that governs recall at inference. It is tempting because class imbalance often depresses recall, and it would be correct if the training data itself were skewed.
- ✗
Increase the threshold to 0.7
Why it's wrong here
Raising the threshold to 0.7 makes the model predict positives less often, which lowers recall further and raises precision. It is tempting because threshold tuning is the standard lever for precision-recall trade-offs, and it would be correct if the goal were to reduce false positives.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.