AI0-001 AI Implementation and Operations Practice Question
An operations team runs a computer-vision model that flags manufacturing defects on an assembly line. Auditors require evidence that any single prediction can be reconstructed and explained months later. The team already logs model version, input image hash, and prediction score. Which additional logging practice best satisfies the audit requirement?
⚠ Common exam trap
The trap here is believing that storing raw inputs or aggregate metrics is enough for explainability, when the requirement is per-prediction attribution captured at inference time with the exact model version.
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
✓
Log the preprocessed feature vector, the model version identifier, and the explanation artifacts such as saliency or SHAP values for each inference.
Auditability of an individual prediction requires the exact inputs the model saw, the version of the model that scored them, and the explanation artifacts generated at inference time. Capturing the preprocessed feature vector, model version identifier, and attributions such as SHAP or saliency values lets auditors replay and justify any single decision. Aggregates, raw frames, or reviewer names cannot reconstruct the original reasoning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Log the prediction score together with the operator who reviewed the flagged unit.
Why it's wrong here
Recording a score and a reviewer identifies who acted on the output but not why the model produced it. There is no record of the input features or the feature attributions, so an auditor cannot reconstruct the inference or verify that the explanation was valid at decision time. Human review metadata complements, but does not replace, model-level explanation logging.
- ✗
Log the raw camera frames indefinitely and rely on the current model to regenerate explanations on demand.
Why it's wrong here
Raw frames alone are insufficient because preprocessing steps and the model version may change, so regenerating an explanation later would use different transformations or weights than the original inference. The explanation would describe a new decision, not the one under audit. Storage cost also grows without bound, and no attribution record is preserved at decision time.
- ✓
Log the preprocessed feature vector, the model version identifier, and the explanation artifacts such as saliency or SHAP values for each inference.
Why this is correct
Reconstructing and explaining one prediction requires the exact inputs the model consumed, the precise model version, and the attribution output that shows which features drove the score. Logging the feature vector plus explanation artifacts such as saliency maps or SHAP values gives auditors everything needed to replay and justify that specific decision months later.
- ✗
Log only the aggregate daily defect rate and the model's overall precision.
Why it's wrong here
Aggregate metrics describe population-level behavior and cannot reconstruct or explain an individual prediction. Auditors asking about a specific flagged unit would find no per-instance record of the features or rationale used. Aggregate logging is useful for trend dashboards but fails the requirement that any single prediction be traceable and explainable after the fact.
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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.