AI0-001 Implementing AI Solutions Practice Question
In the AI project lifecycle, after a model is trained and evaluated, it is deployed to a production environment. What is the NEXT critical step to ensure the model continues to perform well over time?
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 model's performance and data drift
Monitoring tracks model performance, data drift, and concept drift in production, triggering retraining or alerts when degradation is detected.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more training data
Why it's wrong here
Data collection is typically done earlier; after deployment, monitoring informs if new data is needed.
- ✗
Archive the model and start a new project
Why it's wrong here
Archiving is not appropriate immediately after deployment; the model is expected to serve predictions.
- ✓
Monitoring the model's performance and data drift
Why this is correct
Continuous monitoring detects performance degradation, data drift, or concept drift, enabling proactive maintenance.
- ✗
Re-train the model from scratch
Why it's wrong here
Retraining should be triggered by monitoring signals, not as an immediate post-deployment step.
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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.