AI0-001 AI Security, Ethics and Governance Practice Test 4 — 15 Questions

Question 1

An organization wants to ensure its AI systems comply with new regulations requiring explanations for automated decisions. Which governance practice is most directly relevant?

Accepted Answer

Deploying explainability tools. Deploying explainability tools (B) is the most directly relevant governance practice because the regulation specifically requires explanations for automated decisions. Explainability tools, such as LIME or SHAP, generate human-interpretable justifications for model outputs, enabling compliance with transparency mandates. This directly addresses the need to understand and communicate why a particular decision was made, unlike other practices that focus on privacy, fairness, or oversight.

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Implementing differential privacy

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Conducting bias audits

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Establishing an AI ethics board

Question 2

A financial institution uses an AI model to approve loans. The model uses features including credit score and ZIP code. During an audit, it is discovered that the model has a high false positive rate for loan default predictions in certain ZIP codes. What should the institution do to address this?

Accepted Answer

Retrain the model with fairness constraints. Retraining the model with fairness constraints directly addresses the root cause of the bias—the model's learned correlations between ZIP code and default risk. Fairness constraints, such as demographic parity or equalized odds, are applied during training to ensure the model's predictions are not systematically skewed against certain groups. This approach preserves the predictive power of legitimate features while mitigating discriminatory outcomes, aligning with AI governance principles.

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Remove the ZIP code feature from the model

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Increase the decision threshold for those ZIP codes

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Discontinue use of the model for those ZIP codes

Question 3

A team is deploying an AI model that predicts patient readmission risk. The model was trained on data from three hospitals but will be used in a fourth hospital with different patient demographics. What is the most important security risk to assess?

Accepted Answer

Adversarial attacks that cause misclassification. Using a model on data from a different distribution (population shift) can degrade performance, but from a security perspective, the main risk is adversarial attacks that exploit the model's unfamiliarity with new data. Model inversion and poisoning are training-time attacks; data breach is an operational risk but not specific to this scenario.

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Data poisoning during training

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Model inversion to extract patient data

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Data breach of the inference API

Question 4

An AI development team is building a system to detect fraudulent transactions. They want to ensure the model complies with regulations requiring that individuals can question automated decisions. Which governance element is most relevant?

Accepted Answer

Right to explanation. The right to explanation is a governance principle that requires automated decision-making systems to provide individuals with meaningful information about how decisions are made. In the context of fraudulent transaction detection, this regulation ensures that a customer can question why a transaction was flagged, and the model must be able to provide an interpretable rationale. This directly aligns with the scenario's requirement for compliance with regulations allowing individuals to question automated decisions.

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Model versioning

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Differential privacy

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Data minimization

Question 5

A security researcher demonstrates that by adding small perturbations to an image of a stop sign, an autonomous vehicle's AI misclassifies it as a speed limit sign. This is an example of which type of attack?

Accepted Answer

Adversarial example attack. This is an adversarial example attack because the researcher adds imperceptible perturbations to the input image (the stop sign) to cause the AI model to output an incorrect classification (speed limit sign). Adversarial examples exploit the model's sensitivity to small, crafted changes in input data, leading to misclassification without altering the underlying task or training data.

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Data poisoning attack

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Model extraction attack

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Membership inference attack

Question 6

Which TWO of the following are common methods for mitigating bias in AI models?

Accepted Answer

Reweighting training samples based on sensitive attributes. Reweighting training samples based on sensitive attributes is a common pre-processing bias mitigation technique. It assigns higher weights to underrepresented groups or lower weights to overrepresented groups to balance the dataset, thereby reducing the model's reliance on biased correlations. This method directly addresses data-level bias before model training begins.

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Using adversarial training

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Applying L1 regularization

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Performing k-fold cross-validation

Question 7

Which THREE of the following are key principles of AI ethics as defined by major frameworks?

Accepted Answer

Transparency. Transparency is a core principle of AI ethics because it requires that AI systems be open about their purpose, data sources, and decision-making processes. Major frameworks like the OECD AI Principles and the EU Ethics Guidelines for Trustworthy AI emphasize transparency to enable auditability and informed consent. Without transparency, stakeholders cannot verify that an AI system operates as intended or identify potential biases.

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Scalability

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Latency

Question 8

Which TWO of the following are effective defenses against adversarial evasion attacks on image classifiers?

Accepted Answer

Gradient masking. Adversarial training and gradient masking (e.g., defensive distillation) are common defenses. Data augmentation helps generalization but not specifically against adversarial perturbations; feature squeezing reduces input complexity; input validation is generic.

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Data augmentation

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Input validation

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Feature squeezing

Question 9

A bank uses an AI model to approve loans. During an audit, it is found that the model denies loans at a higher rate for a certain ethnic group. Which governance principle is primarily violated?

Accepted Answer

Fairness. The model's disparate impact on a specific ethnic group directly violates the principle of Fairness, which requires that AI systems do not discriminate based on protected attributes such as race, ethnicity, or gender. In lending, fairness is often assessed using metrics like demographic parity or equal opportunity, and a higher denial rate for one group indicates a lack of algorithmic fairness.

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Accountability

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Transparency

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Privacy

Question 10

A company deployed an AI chatbot that started generating offensive responses after a data update. The security team needs to quickly mitigate the issue. What should they do first?

Accepted Answer

Disable the chatbot and investigate. The first priority when an AI chatbot generates offensive responses is to stop the harm immediately. Disabling the chatbot (Option B) halts all user interactions, preventing further offensive outputs while the security team investigates the root cause. This aligns with the principle of containment before remediation in incident response.

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Delete the training data

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Roll back to previous model version

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Add a content filter

Question 11

An organization wants to implement an AI ethics board. Which composition best ensures independence and expertise?

Accepted Answer

Mix of internal stakeholders and external ethicists. An AI ethics board must combine internal stakeholders (who understand organizational context, data flows, and operational constraints) with external ethicists (who provide independent, unbiased perspectives and specialized knowledge of ethical frameworks like IEEE Ethically Aligned Design or the EU AI Act). This composition ensures the board can evaluate AI systems for bias, fairness, and transparency without being dominated by business or technical interests, which is critical for maintaining trust and regulatory compliance.

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All members from the legal department

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IT department head and data scientists

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Only senior executives from the company

Question 12

Which practice best ensures AI systems comply with regulations like GDPR?

Accepted Answer

Data minimization and anonymization. Data minimization and anonymization directly align with GDPR's core principles, such as Article 5(1)(c) which mandates that personal data be 'adequate, relevant and limited to what is necessary.' By collecting only essential data and applying techniques like k-anonymity or differential privacy, AI systems reduce the risk of re-identification and ensure compliance with data protection by design and by default (Article 25). This practice is a foundational governance measure, not a reactive or staffing solution.

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Using open-source models only

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Regular vulnerability scans

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Hiring more data scientists

Question 13

An AI model's performance drops significantly in production compared to testing. The data shows distribution shift. What is the best first step?

Accepted Answer

Retrain model with new data. (Retrain model with new data) is correct because retraining with more representative data adapts to distribution shift. Option A (Add more features) may not address the shift. Option C (Change algorithm) is a larger change without addressing data. Option D (Reduce model complexity) might worsen performance.

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Add more features

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Use a different algorithm

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Reduce model complexity

Question 14

An organization uses an AI-based hiring tool. To prevent bias, they want to ensure the model's decisions are explainable. Which approach is most suitable?

Accepted Answer

Use a simpler interpretable model like logistic regression. A simpler interpretable model like logistic regression provides inherent transparency—its coefficients directly show the weight and direction of each input feature on the hiring decision. This makes it easy for auditors and stakeholders to verify that the model is not using protected attributes (e.g., race, gender) in a biased way, without needing post-hoc explanation tools. The key is that the model itself is interpretable by design, not just explained after the fact.

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Use reinforcement learning with fairness constraints

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Use a black-box deep learning model with SHAP explanations

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Use ensemble methods with feature importance

Question 15

During a red-team exercise on an AI model, testers successfully extracted training data. Which vulnerability is this?

Accepted Answer

Model inversion. Model inversion attacks allow an adversary to reconstruct training data by exploiting the model's learned representations. In this scenario, the testers successfully extracted training data, which is the hallmark of a model inversion attack, not just inferring membership.

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Membership inference

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Adversarial example

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Data poisoning