AI0-001 AI Infrastructure and Technologies Practice Question
A hospital's radiology department is deploying an AI system that analyzes chest X-rays to flag potential pneumonia. Because patient data cannot leave the hospital's on-premises network, the model must run locally. The IT team wants to ensure the model's inference results can be explained to radiologists and auditors. Which approach best satisfies the explainability requirement while keeping the model on-premises?
⚠ Common exam trap
The trap here is assuming that a confidence score or a simpler model is sufficient for explainability, when the scenario demands insight into feature contributions while preserving model performance and on-premises data handling.
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
✓
Apply LIME or SHAP to generate local explanations for each prediction without modifying the model.
Post-hoc explainability techniques such as LIME and SHAP allow clinicians to understand individual predictions from complex models without sacrificing accuracy. They operate locally and do not require moving data off-premises, satisfying both the explainability and data residency requirements. Retraining a simpler model risks accuracy, while confidence scores and cloud dashboards fail to provide the needed transparency under the given constraints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a black-box deep learning model and provide a confidence score for each prediction.
Why it's wrong here
A confidence score alone does not explain why a prediction was made; it only indicates the model's certainty. Radiologists and auditors need to understand which image regions influenced the decision. This approach fails the explainability requirement because it offers no insight into feature importance or decision rationale, leaving the model opaque and difficult to justify in a clinical or regulatory review.
- ✓
Apply LIME or SHAP to generate local explanations for each prediction without modifying the model.
Why this is correct
LIME and SHAP are post-hoc explainability techniques that approximate how a model's features contribute to individual predictions. They work with any black-box model and can run entirely on-premises, satisfying the data residency constraint. Radiologists can see which pixels or regions influenced a flag, and auditors gain documentation for compliance. This directly meets the requirement without retraining or altering the deployed model.
- ✗
Deploy the model to a cloud service that offers built-in explainability dashboards.
Why it's wrong here
The hospital explicitly requires that patient data cannot leave the on-premises network. Sending X-rays or model inputs to a cloud service would violate this constraint and potentially data protection regulations. Even if the cloud service provides excellent explainability tools, the data residency requirement makes this option non-compliant. On-premises explainability methods are necessary here.
- ✗
Replace the deep learning model with a logistic regression classifier trained on the same data.
Why it's wrong here
Logistic regression is inherently interpretable, but it often cannot capture the complex spatial patterns in chest X-rays that a convolutional network can. Swapping models may degrade diagnostic accuracy significantly. The scenario requires explainability, not a change in model family. A post-hoc explanation method preserves the high-performing model while adding interpretability, which is a better engineering trade-off.
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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.