AI0-001 AI Implementation and Operations Practice Question
An operations team runs a real-time fraud-scoring model behind a REST endpoint. Latency is acceptable, but over three weeks the model's predicted positive rate has drifted upward even though the model binary and the feature-extraction code have not changed. The team wants to detect and localize this drift before it degrades business outcomes. Which approach should the team implement?
⚠ Common exam trap
The trap here is assuming that any change in model output must be fixed by retraining or by scaling infrastructure, when unchanged code plus changed behavior points to data drift that must first be detected and localized.
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
✓
Configure statistical drift monitoring on the model's input features and on the distribution of predicted scores, with alerting thresholds tied to the training baseline.
The model binary and feature code are stable, so the rising positive rate points to a change in the data reaching the endpoint. Monitoring input-feature distributions and the distribution of predicted scores against the training baseline is the standard way to detect, quantify, and localize that kind of drift, and it gives operations an alerting signal that precedes business impact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure statistical drift monitoring on the model's input features and on the distribution of predicted scores, with alerting thresholds tied to the training baseline.
Why this is correct
Because the model artifact and feature code are unchanged, the shift must originate in the data reaching the endpoint. Feature and prediction-distribution monitoring compares live inputs and outputs against the training baseline, exposing which features moved and whether the score distribution widened. This localizes the drift and triggers an alert before the degraded predictions cause measurable business harm.
- ✗
Retrain the model on the most recent 30 days of labeled data and redeploy it behind the existing endpoint.
Why it's wrong here
Retraining may eventually be the remedy, but doing it first skips detection and diagnosis. Without monitoring, the team cannot prove drift occurred, identify which features shifted, or know whether the new labels are representative. Blind retraining risks masking a data-pipeline defect and produces no evidence for the governance record.
- ✗
Increase the inference service's replica count and enable request batching to smooth out traffic spikes.
Why it's wrong here
Scaling replicas and batching address throughput and latency, not statistical drift. The scenario states latency is already acceptable and the model binary is unchanged, so adding capacity cannot explain or correct a rising predicted positive rate. This action would consume budget while leaving the actual distributional shift undetected and unlocalized.
- ✗
Add a canary deployment that routes a small percentage of live traffic to a shadow model for comparison.
Why it's wrong here
A shadow or canary model compares a candidate model's behavior against production, which is useful for release validation. Here the production model itself is drifting because its inputs changed, so a shadow copy fed the same drifted data would drift identically. It does not measure input or prediction distribution shifts against the training baseline.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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