AI-300 ML Model Lifecycle And Operations Practice Question
You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?
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
✓
Model prediction precision
Accuracy and precision are key performance indicators that signify model decay.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CPU usage of the inference cluster
Why it's wrong here
This is a hardware health metric, not a model performance metric.
- ✗
Network latency
Why it's wrong here
Network latency measures performance, not prediction quality.
- ✗
Number of active users
Why it's wrong here
Usage volume does not indicate model quality.
- ✓
Model prediction precision
Why this is correct
Declining precision indicates the model is failing to identify classes correctly.
- ✓
Model prediction accuracy
Why this is correct
A drop in accuracy directly triggers the need for retraining.
About these practice questions
This AI-300 question is part of Courseiva's 204-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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-300 practice question is part of Courseiva's free Microsoft 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 AI-300 exam.