AI0-001 AI Implementation and Operations Practice Question
A team deploys a machine learning model as a REST API. They want to monitor model drift. Which metric is MOST appropriate for detecting drift in the input data distribution?
⚠ Common exam trap
Watch out — candidates often confuse performance metrics (accuracy, F1, RMSE) with distribution drift detection, not realizing that PSI specifically quantifies covariate shift without needing ground truth labels.
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
✓
Population stability index (PSI) comparing training and recent data.
Population stability index (PSI) is the most appropriate metric for detecting drift in input data distribution because it directly measures the shift between the training data distribution and the recent production data distribution. PSI is calculated by binning both distributions and computing the sum of (proportion in bin of recent data minus proportion in bin of training data) times the natural log of their ratio, making it sensitive to changes in feature distributions without requiring ground truth labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model accuracy on a recent holdout set.
Why it's wrong here
Accuracy on a recent holdout set measures label agreement, requiring ground-truth labels that arrive late or not at all in production, and it detects concept drift rather than input-distribution shift. It tempts because accuracy is the intuitive health metric, and it would be correct when labelled outcomes are available for periodic evaluation.
- ✓
Population stability index (PSI) comparing training and recent data.
Why this is correct
PSI quantifies how much a variable's distribution has shifted between the training baseline and recent production data, which is exactly the input-distribution drift the team must detect. It is computed on features rather than predictions, unlike accuracy or label-based metrics.
- ✗
F1 score on the training data.
Why it's wrong here
F1 score on training data measures classification quality against labels the model already saw, so it cannot reveal a shift in incoming feature distributions. It tempts because F1 is a standard classification metric, and it would be correct when evaluating a classifier's precision-recall balance on a labelled evaluation set.
- ✗
Root mean squared error (RMSE) on test data.
Why it's wrong here
RMSE on test data quantifies prediction error against known labels, not changes in the distribution of live input features. It tempts because RMSE is the standard regression error metric, and it would be correct when assessing how far a regressor's numeric predictions deviate from ground truth.
About these practice questions
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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.