AI0-001 AI Implementation and Operations Practice Question
A data scientist is deploying a machine learning model to production. The model was trained on an imbalanced dataset. Which technique should be used during deployment to mitigate bias without retraining the model?
⚠ Common exam trap
CompTIA often tests the distinction between techniques applied during training versus deployment, and the trap here is that candidates mistakenly choose SMOTE or ensemble methods, which require retraining, instead of recognizing that threshold adjustment is a valid post-deployment bias mitigation strategy.
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
✓
Apply post-processing calibration to adjust decision thresholds
Post-processing calibration adjusts the decision threshold of the model to account for the class imbalance present in the training data. This technique modifies the output probabilities or classification boundary without requiring access to the original training data or retraining the model, making it suitable for deployment scenarios where the model is already fixed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply post-processing calibration to adjust decision thresholds
Why this is correct
Post-processing calibration adjusts thresholds to improve fairness without retraining.
- ✗
Use an ensemble of models trained on balanced subsets
Why it's wrong here
Ensemble requires retraining, not available at deployment.
- ✗
Rebalance the dataset using SMOTE before inference
Why it's wrong here
SMOTE is a training-time technique, not for deployment.
- ✗
Remove sensitive features from the input data
Why it's wrong here
Removing features may not correct bias from imbalance.
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Written by Johnson Ajibi, MSc IT Security
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