AI0-001 Implementing AI Solutions Practice Question
A machine learning engineer is deploying a production model that requires strict monitoring. Which TWO monitoring strategies should be implemented to detect data drift and model degradation? (Choose TWO.)
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
✓
Monitoring the distribution of model predictions
Tracking prediction distribution shifts and monitoring feature distribution over time help detect drift and degradation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Logging all input data for manual review
Why it's wrong here
Manual review is not scalable and does not provide automated drift detection.
- ✗
Monitoring prediction confidence scores over time
Why it's wrong here
Confidence scores may not reliably indicate drift; a model can be confident yet wrong.
- ✓
Monitoring the distribution of model predictions
Why this is correct
Shifts in prediction distribution can signal model decay or data drift.
- ✓
Monitoring input feature distribution (e.g., via PSI)
Why this is correct
Population Stability Index (PSI) detects changes in feature distributions, indicating data drift.
- ✗
Retraining the model weekly as a routine
Why it's wrong here
Retraining is an action, not a monitoring strategy.
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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.