Quick Answer
The correct answer is SHAP (SHapley Additive exPlanations) because it provides per-feature attribution for individual predictions, making it ideal for explaining why a specific review was classified as negative. SHAP values are grounded in cooperative game theory, ensuring a fair distribution of each feature’s contribution to the model’s output, which offers both local interpretability and theoretical rigor. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish between explainable AI techniques for model transparency—a common trap is confusing SHAP with LIME, but remember that while both offer local explanations, SHAP is more mathematically grounded and consistent. Another pitfall is mistaking t-SNE (a visualization tool) or decision trees (a model type) for explanation methods. A helpful memory tip: think of SHAP as “SHapley Additive” where each feature gets its fair “share” of the prediction, just like players in a game.
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.
A data scientist trains a sentiment analysis model on user reviews. To ensure transparency, they want to explain why the model classified a particular review as negative. Which explainability technique should they use?
Decision tree surrogate model
Why wrong: A surrogate can approximate but is less direct than SHAP.
Principal component analysis
Why wrong: PCA reduces dimensions but does not explain model output.
SHAP (SHapley Additive exPlanations)
SHAP computes feature contributions for each prediction.
t-SNE dimensionality reduction
Why wrong: t-SNE is for visualization, not explanation of individual predictions.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SHAP (SHapley Additive exPlanations)
Option D is correct because SHAP values provide per-feature attribution for individual predictions. Option A is wrong because LIME is also for local explanations, but SHAP is more theoretically grounded and common for feature attribution. Option B is wrong because t-SNE is for visualization of high-dimensional data, not explanation. Option C is wrong because decision trees are a model type, not an explanation method for any model.
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
For each option: why learners choose it and why it is or isn't the right answer here.
Decision tree surrogate model
Why it's wrong here
A surrogate can approximate but is less direct than SHAP.
Principal component analysis
Why it's wrong here
PCA reduces dimensions but does not explain model output.
SHAP (SHapley Additive exPlanations)
Why this is correct
SHAP computes feature contributions for each prediction.
Related concept
Static NAT maps one inside address to one outside address.
t-SNE dimensionality reduction
Why it's wrong here
t-SNE is for visualization, not explanation of individual predictions.
Common exam traps
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.
Trap categories for this question
Command / output trap
PCA reduces dimensions but does not explain model output.
Detailed technical explanation
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.
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
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
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.
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FAQ
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Static NAT maps one inside address to one outside address..
The correct answer is: SHAP (SHapley Additive exPlanations) — Option D is correct because SHAP values provide per-feature attribution for individual predictions. Option A is wrong because LIME is also for local explanations, but SHAP is more theoretically grounded and common for feature attribution. Option B is wrong because t-SNE is for visualization of high-dimensional data, not explanation. Option C is wrong because decision trees are a model type, not an explanation method for any model.
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.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 2026
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