AI0-001 AI Concepts and Foundations Practice Question
Exhibit
Refer to the exhibit.
JSON Policy for Model Deployment:
{
"model": "sentiment_analysis_v2",
"threshold": 0.7,
"fairness_check": {
"protected_attributes": ["gender", "age_group"],
"metric": "demographic_parity",
"tolerance": 0.05
},
"explainability": {
"method": "LIME",
"num_features": 5
},
"monitoring": {
"drift_detection": {
"feature_drift": true,
"prediction_drift": true,
"alert_threshold": 0.2
}
}
}Refer to the exhibit. A team deploys a sentiment analysis model with this policy. After one month, the monitoring system triggers an alert for feature drift. Which action should the team take first?
⚠ Common exam trap
CompTIA often tests the misconception that any model alert should trigger immediate retraining, but the correct first step is always to diagnose the drift type and affected features before taking action.
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
✓
Compare the current feature distributions with the training set to identify which features drifted.
When a monitoring system triggers an alert for feature drift, the first step is to diagnose which features have changed. Comparing current feature distributions with the training set identifies the specific features that drifted, enabling targeted remediation such as retraining with recent data or feature engineering. This aligns with the standard MLOps workflow for drift detection and response.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Review the fairness check settings to ensure protected attributes are still relevant.
Why it's wrong here
Feature drift concerns input data distributions shifting, not protected-attribute fairness, so fairness settings cannot explain or resolve the alert. It is tempting because fairness monitoring is part of the same policy, and would be the right first step for a bias or disparate-impact alert.
- ✗
Immediately retrain the model on recent data to adapt to the drift.
Why it's wrong here
Retraining immediately treats the symptom without diagnosing the cause; drift alerts require investigating data quality, pipeline changes or upstream schema shifts first, since retraining on corrupted recent data bakes in the fault. Retraining is the right response once drift is confirmed as genuine concept change rather than a data-collection defect.
- ✓
Compare the current feature distributions with the training set to identify which features drifted.
Why this is correct
Feature drift means input distributions have shifted away from the training data, so the first step is diagnostic: compare current feature distributions against the training set to identify which features drifted and by how much, before deciding whether to retrain or adjust monitoring thresholds.
- ✗
Reduce the classification threshold to 0.5 to increase sensitivity.
Why it's wrong here
Lowering the threshold alters prediction sensitivity, not the drifted input features, so it masks rather than addresses the drift. It is tempting because threshold tuning is a quick lever for shifting precision and recall, and would be correct when the alert concerns class imbalance or recall degradation.
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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.