AI0-001 AI Implementation and Operations Practice Question
An AI system experiences degraded accuracy over time due to changes in user behavior. Which monitoring metric should be prioritized to detect this issue earliest?
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
✓
Data drift detection on input features
Data drift detection monitors changes in input distribution, which often precedes accuracy drop. Option A is wrong because accuracy is a lagging indicator. Option C is wrong because latency doesn't reflect data shift. Option D is wrong because AUC is also lagging.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
API response latency
Why it's wrong here
Latency is unrelated to data distribution.
- ✓
Data drift detection on input features
Why this is correct
Data drift detects changes before performance degrades.
- ✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC is also a lagging performance metric.
- ✗
Model accuracy on a holdout validation set
Why it's wrong here
Accuracy drops after drift has already occurred.
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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.