AI0-001 AI Implementation and Operations Practice Question
A hospital's radiology department uses an AI model to detect lung nodules in CT scans. The model was trained on data from a specific brand of scanners and patient demographics common in Europe. Recently, the hospital acquired new scanners from a different manufacturer and started serving a more diverse patient population. Over the past month, the model's false-positive rate has increased by 15% and false-negative rate by 8%. The radiologists are losing confidence and are considering abandoning the AI tool altogether. The IT team has verified that the model inference is running correctly and the hardware is performing as expected. The data science team suspects the problem is related to the change in input data distribution. The hospital's AI operations policy requires that any model update must be validated on at least 500 recent cases before deployment. What is the BEST course of action for the AI operations team?
⚠ Common exam trap
AI0-001 often tests concepts of data drift and model retraining. Candidates may choose to adjust the threshold or roll back, but the trap is not recognizing that the root cause is distribution shift, which requires retraining with new data.
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
✓
Collect 500 recent CT scans from the new scanners, retrain the model on a combined old and new dataset, and validate before deployment.
The model's performance degradation is likely due to data drift: the input data distribution has changed because of new scanners and a more diverse patient population. The best course is to collect recent data representative of the new distribution, retrain the model on a combined dataset (old and new), and validate on at least 500 recent cases as per policy. This addresses the root cause and ensures the model generalizes to the new data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Roll back to the previous model version and restrict use of the AI tool to only European patients.
Why it's wrong here
Restricting use by nationality does not address the distribution shift from the new scanner manufacturer, and rolling back reinstates a model trained on the same narrow data. It appeals because rollback is a standard safe fallback, but here the trigger is covariate shift, not a faulty deployment.
- ✓
Collect 500 recent CT scans from the new scanners, retrain the model on a combined old and new dataset, and validate before deployment.
Why this is correct
Data drift from new scanners and demographics explains the degraded metrics, so retraining on a combined old and new dataset restores generalisation. Collecting 500 recent scans satisfies the policy's validation requirement, and validating before deployment confirms the fix works.
- ✗
Retrain the model using the original training data but with increased regularization to avoid overfitting.
Why it's wrong here
Retraining on the original data with added regularisation cannot correct distribution shift from new scanners and demographics; the model needs representative recent data. It is tempting because regularisation addresses overfitting, and would be correct if the model were overfitting its original training set rather than facing shifted inputs.
- ✗
Adjust the model's decision threshold to reduce false positives and then monitor for two weeks.
Why it's wrong here
Threshold tuning only trades false positives against false negatives along one curve; it cannot correct the shifted feature distribution causing both rates to rise. It is tempting because threshold adjustment is a legitimate calibration step, but that applies when the operating point, not the input distribution, is the problem.
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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
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.