AI0-001 AI Security, Ethics and Governance Practice Test 5 — 15 Questions

Question 1

Which TWO are key requirements for AI governance under the EU AI Act for high-risk AI systems? (Choose two.)

Accepted Answer

Human oversight. The EU AI Act mandates that high-risk AI systems must incorporate human oversight mechanisms to ensure that humans can intervene or override the system's decisions when necessary. This requirement is designed to prevent or minimize risks to health, safety, and fundamental rights, and it is a core governance obligation under Article 14 of the Act.

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Regular performance benchmarks

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Open-source licensing

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Mandatory use of cloud

Question 2

Which THREE are effective methods for ensuring data privacy in AI training? (Choose three.)

Accepted Answer

Data anonymization. Data anonymization (B) is correct because it removes or obfuscates personally identifiable information (PII) from training datasets, ensuring that individuals cannot be re-identified. This is a foundational privacy technique that directly addresses regulatory requirements like GDPR and CCPA by breaking the link between data and specific individuals.

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Data encryption at rest

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Data replication

Question 3

Which TWO are common attack vectors against AI systems? (Choose two.)

Accepted Answer

Data poisoning. Data poisoning is a common attack vector against AI systems where an attacker manipulates the training data to corrupt the model's learning process. By injecting malicious or misleading samples into the training dataset, the attacker can cause the model to learn incorrect patterns, leading to biased outputs, reduced accuracy, or targeted misclassifications. This directly targets the integrity of the AI's training pipeline.

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SQL injection

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Cross-site scripting

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Buffer overflow

Question 4

Refer to the exhibit. An auditor reports that the model's fairness check was bypassed in a recent deployment. Based on the policy, what is the most likely cause?

Accepted Answer

Data scientist role has deploy permission, allowing deployment without fairness validation. (Data scientist role has deploy permission, allowing deployment without fairness validation) is correct. The policy shows fairness_check required, but if the deployment process does not enforce it, the data scientist could bypass it. Option A (Auditor lacks evaluate) is unrelated to deployment. Option C (Fairness threshold low) does not cause bypass. Option D (External user inference) is unrelated.

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The auditor role lacks 'evaluate' permission

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Fairness check threshold is set to 0.8, which is too low

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External_user role can perform inference, which triggers unfair predictions

Question 5

Refer to the exhibit. An AI governance review finds that a model was deployed without required ethics approval. Based on the audit log, who is most responsible for the compliance failure?

Accepted Answer

Bob. Bob is the data scientist who deployed the model to production. The audit log shows that Bob executed the deployment command without first obtaining the required ethics approval. As the individual who performed the action that violated the governance policy, Bob bears primary responsibility for the compliance failure.

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Alice

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Carol

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System

Question 6

Refer to the exhibit. A security auditor identifies a critical vulnerability that could allow an attacker to manipulate model inputs to cause misclassification. Which configuration setting is most directly responsible for this vulnerability?

Accepted Answer

enable_adversarial_defense = false. The vulnerability described is an adversarial attack on model inputs, which directly exploits the absence of adversarial defenses. Setting `enable_adversarial_defense = false` disables mechanisms like adversarial training or input perturbation detection that prevent misclassification from manipulated inputs. This configuration is the most direct root cause because it explicitly turns off the defense designed to counter such attacks.

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enable_input_sanitization = true

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audit_level = basic

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pii_detection = enabled

Question 7

A healthcare AI system used for diagnosis shows a significant accuracy difference between demographic groups. Which technique should be applied to directly reduce this bias during model training?

Accepted Answer

Apply adversarial debiasing during training. Adversarial debiasing directly reduces bias during model training by introducing an adversarial network that attempts to predict the protected attribute (e.g., demographic group) from the model's predictions. The primary model is trained to maximize accuracy while simultaneously minimizing the adversary's ability to infer the protected attribute, thereby forcing the model to learn representations that are invariant to that attribute. This technique directly addresses the accuracy disparity by encoding fairness as an optimization objective, unlike post-hoc or data-level approaches.

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Ignore the disparity as long as overall accuracy is acceptable

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Retrain the model with more data from the underperforming group

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Remove demographic attributes from the training data

Question 8

Which principle ensures that AI decisions can be traced back and understood by humans?

Accepted Answer

Transparency. Transparency is the principle that ensures AI decisions can be traced back and understood by humans. It requires that the internal workings of an AI model, including its inputs, decision paths, and outputs, are documented and interpretable, enabling auditability and trust. Without transparency, stakeholders cannot verify whether the AI system is behaving as intended or complying with ethical and regulatory standards.

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Privacy

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Robustness

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Accountability

Question 9

A financial institution uses a deep learning model for loan approvals. Under the EU AI Act, this is considered a high-risk AI system. Which mandatory requirement must the institution fulfill before deployment?

Accepted Answer

Conduct a risk assessment and bias testing. Under the EU AI Act, high-risk AI systems must undergo a conformity assessment that includes a risk assessment and bias testing to ensure fairness, transparency, and non-discrimination before deployment. This requirement is mandated by Articles 9 and 10 of the Act, which specifically address risk management and data governance for high-risk systems. Option D correctly identifies this mandatory step, as the institution must demonstrate that the model does not produce biased outcomes that could lead to discriminatory lending practices.

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Obtain certification from an ISO 27001 auditor

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Publish the model's source code publicly

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Register the AI system with the national data protection authority

Question 10

An image classification model misclassifies a stop sign as a speed limit sign after a few pixels are altered. What is the most effective defense against such attacks?

Accepted Answer

Adversarial training. Adversarial training is the most effective defense because it explicitly incorporates adversarial examples—like the perturbed stop sign—into the model's training data. By training on both clean and adversarially altered images, the model learns to be robust against small, malicious perturbations that cause misclassification. This directly addresses the root cause of the vulnerability, unlike other options that only mitigate symptoms or ignore the attack vector.

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Use a larger validation dataset

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Reduce the input image resolution

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Increase the model's complexity

Question 11

Which ethical concern is most directly associated with AI systems that fully automate decision-making without human oversight?

Accepted Answer

Loss of human autonomy. Lack of human oversight can lead to ethical issues like biased decisions or unfair outcomes without recourse.

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Data storage costs

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Job displacement

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Environmental impact

Question 12

A research lab trains a language model using DP-SGD. What primary privacy risk does this technique mitigate?

Accepted Answer

Membership inference attacks. DP-SGD (Differentially Private Stochastic Gradient Descent) mitigates membership inference attacks by adding calibrated noise to gradients during training, which bounds the influence any single training example can have on the final model. This differential privacy guarantee makes it difficult for an adversary to determine whether a specific data point was included in the training set, directly addressing the core risk of membership inference.

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Data poisoning attacks

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Adversarial patch attacks

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Model inversion attacks

Question 13

Which AI governance framework is specifically designed by the U.S. National Institute of Standards and Technology (NIST) to help organizations manage AI risks?

Accepted Answer

NIST AI Risk Management Framework. The NIST AI Risk Management Framework (AI RMF) is the specific governance framework developed by the U.S. National Institute of Standards and Technology to help organizations manage AI risks, including those related to trustworthiness, fairness, and robustness. It provides a structured approach for identifying, assessing, and mitigating risks throughout the AI lifecycle, aligning with NIST's role in setting standards for cybersecurity and risk management.

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ISO/IEC 27001

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COBIT

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GDPR

Question 14

What is the primary function of an AI ethics board within an organization?

Accepted Answer

Reviewing AI projects for ethical compliance. The primary function of an AI ethics board is to review AI projects for ethical compliance, ensuring that the organization's AI systems adhere to established ethical principles, legal standards, and governance frameworks. This board typically assesses risks related to bias, fairness, transparency, and accountability before deployment, rather than engaging in technical development or operational tasks.

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Developing algorithms

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Managing cloud infrastructure

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Marketing AI products

Question 15

After deploying a model for fraud detection, the data scientist observes a steady decline in precision over two months. Which issue is most likely occurring?

Accepted Answer

Concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's decision boundary to become outdated. In fraud detection, fraudsters continuously adapt their methods, so the relationship between input features and the fraud label shifts, leading to a steady decline in precision as false positives increase.

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Data drift

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Model overfitting

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Adversarial attack