AI0-001 AI Security, Ethics and Governance Practice Question
A financial services firm uses an AI model to detect fraudulent transactions. The model's decisions must be explainable to regulators. The data science team proposes using a complex deep neural network with high accuracy. Which of the following approaches best balances accuracy and explainability?
⚠ Common exam trap
The trap here is assuming that high accuracy and explainability are mutually exclusive, leading to an unnecessary trade-off.
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
✓
Use the deep neural network and apply post-hoc explanation techniques such as LIME or SHAP.
Post-hoc explanation techniques such as LIME and SHAP offer a practical compromise: they explain individual predictions of complex models without requiring the model to be inherently interpretable. This allows the firm to maintain high fraud detection accuracy while providing the transparency regulators demand. Replacing the model with a simpler one may reduce accuracy, and providing only overall metrics is insufficient.
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 the deep neural network without explanations but provide regulators with the model's overall accuracy metrics.
Why it's wrong here
Overall accuracy metrics do not explain individual decisions, which regulators often require to ensure fair and non-discriminatory outcomes. This approach fails to meet explainability requirements and could lead to regulatory penalties. Transparency at the individual level is crucial in financial services.
- ✗
Train a surrogate decision tree to mimic the neural network's predictions and use that for explanations.
Why it's wrong here
A surrogate decision tree can approximate the neural network but may not faithfully represent its decision boundaries, leading to misleading explanations. It also adds complexity and potential inaccuracies. Post-hoc methods like LIME and SHAP are more directly tied to the model's behavior and are preferred for regulatory contexts.
- ✗
Replace the deep neural network with a simple logistic regression model to ensure full interpretability.
Why it's wrong here
While logistic regression is inherently interpretable, it may not capture complex fraud patterns, leading to lower accuracy and increased financial losses. The firm should not sacrifice significant accuracy when post-hoc explanations can provide sufficient transparency. A balance is needed, not a wholesale replacement.
- ✓
Use the deep neural network and apply post-hoc explanation techniques such as LIME or SHAP.
Why this is correct
Post-hoc explanation methods like LIME and SHAP can provide local explanations for individual predictions without sacrificing the accuracy of the complex model. While they are approximations, they are widely accepted for regulatory purposes when combined with documentation. This approach allows the firm to leverage the high accuracy of deep learning while meeting explainability requirements.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.