This AI0-001 practice question tests your understanding of ai implementation and operations. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Model accuracy on a recent holdout set.
Why wrong: Accuracy drop indicates drift but does not isolate input drift.
Population stability index (PSI) comparing training and recent data.
PSI directly quantifies distribution shift.
F1 score on the training data.
Why wrong: F1 score is a performance metric, not a drift metric.
Root mean squared error (RMSE) on test data.
Why wrong: RMSE measures prediction error, not input drift.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 drop indicates drift but does not isolate input drift.
Population stability index (PSI) comparing training and recent data.
Why this is correct
PSI directly quantifies distribution shift.
Related concept
Read the scenario before looking for a memorised answer.
F1 score on the training data.
Why it's wrong here
F1 score is a performance metric, not a drift metric.
Root mean squared error (RMSE) on test data.
Why it's wrong here
RMSE measures prediction error, not input drift.
Common exam traps
The trap here is that 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.
Detailed technical explanation
PSI is computed by dividing the expected (training) and actual (recent) distributions into k bins (typically 10), then applying the formula: PSI = Σ (Actual_i - Expected_i) * ln(Actual_i / Expected_i). A PSI value less than 0.1 indicates no significant shift, 0.1 to 0.25 suggests moderate drift, and greater than 0.25 signals severe drift requiring investigation. In real-world scenarios, PSI is often monitored per feature or for the overall model score distribution to trigger retraining pipelines.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
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FAQ
AI Implementation and Operations — This question tests AI Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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