The answer is SHAP and LIME, as these are the two common techniques to improve the transparency and interpretability of an AI model. Both are model-agnostic interpretability methods, meaning they can explain predictions from any machine learning model without needing access to its internal structure. SHAP values use game theory to assign each feature a contribution score for a specific prediction, while LIME builds a simple, local surrogate model around that prediction to approximate the black box’s behavior. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish interpretability tools from model types or unrelated concepts—a common trap is confusing a specific algorithm like random forest with a general explanation technique. Remember the memory tip: SHAP and LIME are the “explainers,” not the models themselves.
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which TWO of the following are common techniques to improve the transparency and interpretability of an AI model?
Options B and D are correct. SHAP values and LIME are model-agnostic interpretability methods. Option A is wrong because deep learning models are often black boxes. Option C is wrong as random forest is a specific model, not a technique. Option E is wrong because differential privacy is for privacy, not interpretability.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
LIME approximates the model locally to provide explanations.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI0-001 NAT questions on configuration and troubleshooting.
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Generate SHAP (SHapley Additive exPlanations) values — Options B and D are correct. SHAP values and LIME are model-agnostic interpretability methods. Option A is wrong because deep learning models are often black boxes. Option C is wrong as random forest is a specific model, not a technique. Option E is wrong because differential privacy is for privacy, not interpretability.
What should I do if I get this AI0-001 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI0-001 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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