AI0-001 AI Implementation and Operations Practice Question
A healthcare AI startup has developed a model to detect diabetic retinopathy from retinal images. The model achieved 96% sensitivity and 94% specificity on a validation set from the same distribution as the training data. After deployment in a rural clinic, the model's sensitivity drops to 80%. The data team analyzes the clinical images from the clinic and finds that the images have lower resolution and different lighting conditions compared to the training dataset. The team has the ability to collect more data from the clinic and retrain the model. What is the BEST course of action?
⚠ Common exam trap
CompTIA often tests the misconception that reducing model complexity or using generic transfer learning can fix domain shift, when in reality the most reliable solution is to retrain with data from the target deployment environment.
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 additional retinal images from the rural clinic, label them, and retrain the model including the new data.
The performance drop is caused by a domain shift (lower resolution, different lighting) between the training and deployment data. The most direct and effective solution is to collect labeled images from the target domain (rural clinic) and retrain the model, which aligns with the principle of domain adaptation through data augmentation. This approach addresses the root cause by exposing the model to the actual distribution it will encounter in production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the model's complexity by removing several convolutional layers to improve generalization.
Why it's wrong here
Reducing complexity may hurt overall performance and does not address the specific domain shift.
- ✗
Apply transfer learning using a model pre-trained on a different medical imaging dataset.
Why it's wrong here
Transfer learning may help but is less targeted than retraining on actual clinic data.
- ✗
Implement adversarial validation to identify which images are out-of-distribution and filter them out.
Why it's wrong here
Filtering out images reduces the usable sample size and does not improve model robustness.
- ✓
Collect additional retinal images from the rural clinic, label them, and retrain the model including the new data.
Why this is correct
Adding data from the target domain re-aligns the model with the deployment environment.
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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.