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?
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.
Collecting 500 recent CT scans from the new scanners and retraining on a combined dataset addresses the data drift (change in scanner brand and patient demographics). This approach updates the model to the new distribution while retaining knowledge from the original data, and it complies with the validation requirement of 500 recent cases. Option A is wrong because rolling back and restricting use does not solve the underlying issue and limits utility. Option C is wrong because retraining on the original data with increased regularization does not incorporate the new data distribution, so it would not fix the performance degradation. Option D is wrong because adjusting the decision threshold only trades off false positives and false negatives without addressing the root cause of data drift, and it does not involve retraining or validation as per policy.
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
Rolling back does not solve the problem for the new patient population and scanner; restricting use reduces clinical value.
- ✓
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
Retraining with a representative sample addresses the data drift and meets the policy requirement of 500 validation cases.
- ✗
Retrain the model using the original training data but with increased regularization to avoid overfitting.
Why it's wrong here
Retraining on old data alone will not capture the new distribution; regularization does not adapt to domain shift.
- ✗
Adjust the model's decision threshold to reduce false positives and then monitor for two weeks.
Why it's wrong here
Threshold adjustment can reduce false positives but may increase false negatives and does not address the underlying data shift.
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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.