AI-103 Plan And Manage AN Azure AI Solution Practice Question
Which TWO monitoring strategies are effective for detecting 'data drift' in a deployed AI model?
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
✓
Tracking changes in input data distribution.
Monitoring input data distributions and model performance metrics are the two primary ways to detect drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Tracking changes in input data distribution.
Why this is correct
Significant shifts in input data are a primary indicator of drift.
- ✓
Analyzing model prediction accuracy against ground truth.
Why this is correct
A drop in accuracy often indicates that the model is no longer aligned with current data.
- ✗
Checking the physical temperature of the CPU.
Why it's wrong here
Hardware temperature is unrelated to model drift.
- ✗
Monitoring total API request volume.
Why it's wrong here
Volume tracks usage, not the quality of the model predictions.
- ✗
Restarting the endpoint every hour.
Why it's wrong here
Restarting does not detect drift.
About these practice questions
One of 510 original AI-103 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-103 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-103 exam.