AI0-001 Implementing AI Solutions Practice Question
An organization is deploying an image classification model to detect defects on a production line. Which TWO steps are essential during the model monitoring phase of the AI project lifecycle?
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
✓
Track model performance metrics such as precision and recall on a held-out test set over time
Monitoring should track data drift (changes in input distribution) and model drift (performance degradation). Retraining is an action after monitoring, not a step of monitoring itself. Data preparation is completed before deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Track model performance metrics such as precision and recall on a held-out test set over time
Why this is correct
Tracking metrics on live data (or periodic golden sets) identifies model drift.
- ✗
Perform data cleaning and normalization on the production data before inference
Why it's wrong here
Data cleaning is part of data preparation, not real-time monitoring.
- ✗
Periodically retrain the model with newly labeled data
Why it's wrong here
Retraining is a response to monitoring findings, not a monitoring step itself.
- ✗
Automatically roll back to the previous model version if a metric drops below a threshold
Why it's wrong here
Rollback is an action triggered by monitoring, not a monitoring step.
- ✓
Monitor for data drift between the training data distribution and incoming production data
Why this is correct
Data drift detection alerts when new data differs from training, indicating potential performance issues.
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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.