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
Fairness is separate; drift detection triggered due to feature drift, not fairness.
- ✗
Immediately retrain the model on recent data to adapt to the drift.
Why it's wrong here
Retraining without analyzing the nature of drift may lead to suboptimal model.
- ✓
Compare the current feature distributions with the training set to identify which features drifted.
Why this is correct
Drift analysis should first characterize the drift to decide corrective action.
- ✗
Reduce the classification threshold to 0.5 to increase sensitivity.
Why it's wrong here
Threshold change does not address feature drift.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.