The best course of action is to use a model-agnostic explanation technique like SHAP. SHAP, which stands for SHapley Additive exPlanations, is correct because it provides post-hoc interpretability for any black-box AI model without needing access to its internal structure or proprietary algorithm. This means the bank can meet regulatory explainability requirements while preserving the vendor’s proprietary model and its predictive performance. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of how to balance transparency with performance in AI governance—a common trap is assuming you must sacrifice accuracy for explainability or that you need to open the vendor’s code. Instead, remember that model-agnostic tools like SHAP work on the model’s outputs alone. Memory tip: think of SHAP as a “black-box flashlight”—it shines light on decisions without breaking the box.
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are an AI governance officer at a bank that uses a machine learning model to predict credit risk. The model was developed by an external vendor and uses a proprietary algorithm. The bank's compliance team has determined that the model must be explainable to meet regulatory requirements. However, the vendor claims the model is a 'black box' and cannot provide explanations. You need to ensure compliance while maintaining the model's performance. What is the best course of action?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a model-agnostic explanation technique like SHAP
D is correct because model-agnostic explanation techniques like SHAP (SHapley Additive exPlanations) can provide post-hoc interpretability for any black-box model without requiring access to its internal structure or proprietary algorithm. This allows the bank to meet regulatory explainability requirements while preserving the vendor's proprietary model and its predictive performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Ignore the requirement as the model is proprietary
Why it's wrong here
Regulatory compliance is mandatory; ignoring is not an option.
✗
Ask the vendor to develop a custom explanation module
Why it's wrong here
Vendor already indicated impossibility; custom module may not be feasible.
✗
Replace the model with a simpler, interpretable model
Why it's wrong here
Simpler models may have lower performance, impacting business.
✓
Use a model-agnostic explanation technique like SHAP
Why this is correct
SHAP provides explanations for any model, satisfying regulatory needs.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume that a 'black box' model cannot be explained at all, leading them to choose replacement with a simpler model (Option C), when in fact model-agnostic techniques like SHAP or LIME can provide explanations without altering the model itself.
Detailed technical explanation
How to think about this question
SHAP works by computing Shapley values from cooperative game theory, assigning each feature an importance value for a given prediction by averaging its marginal contribution across all possible feature subsets. This technique is model-agnostic, meaning it can explain any classifier or regressor by treating the model as a black box and only requiring access to the model's prediction function. In practice, SHAP can be computationally expensive for high-dimensional data, but it provides consistent and locally accurate explanations that satisfy regulatory requirements for interpretability.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a model-agnostic explanation technique like SHAP — D is correct because model-agnostic explanation techniques like SHAP (SHapley Additive exPlanations) can provide post-hoc interpretability for any black-box model without requiring access to its internal structure or proprietary algorithm. This allows the bank to meet regulatory explainability requirements while preserving the vendor's proprietary model and its predictive performance.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.