AI0-001 AI Governance and Ethics Practice Test 2 — 15 Questions

Question 1

A company is developing an AI policy for internal use. According to corporate AI governance best practices, which TWO components are essential for the policy?

Accepted Answer

Establishment of an AI ethics board. An AI ethics board is essential because it provides independent oversight to ensure AI systems align with ethical principles, legal requirements, and corporate values. This board reviews high-risk AI use cases, approves ethical impact assessments, and enforces accountability, which is a core requirement of AI governance frameworks like the NIST AI Risk Management Framework.

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Definition of roles and responsibilities for AI development

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Vendor AI assessment procedures

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Data minimization requirements

Question 2

A company wants to adopt green AI practices to reduce the environmental impact of training large models. Which TWO actions are most effective?

Accepted Answer

Use efficient model architectures (e.g., pruning, quantization). Using efficient model architectures (A) and energy-efficient hardware (E) directly reduce energy consumption. Using larger datasets (B) increases energy use. Training models on weekends (C) does not affect total energy consumption. Training models in the cloud (D) may offload energy costs to the provider but does not reduce total energy used.

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Use larger datasets to improve accuracy

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Train models only on weekends

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Train models in the cloud to offload energy costs

Question 3

An insurance company uses a black-box deep learning model to set premiums. Regulators demand explanation for individual decisions. Which interpretability technique should the data science team apply to generate local explanations for each prediction?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct choice because it generates local, interpretable explanations for individual predictions by approximating the black-box model with a simpler, interpretable surrogate model around the specific instance. This directly meets the regulatory requirement for explaining why a particular premium was set for a specific customer, without needing access to the model's internal structure.

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Attention visualization

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Model cards

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SHAP values

Question 4

A company is deploying a generative AI system that produces text content. To comply with emerging transparency obligations, which THREE measures should they implement?

Accepted Answer

Watermark AI-generated content. Watermarking AI-generated content (Option A) is correct because it embeds an imperceptible, machine-detectable signal into the output, enabling provenance verification. This directly addresses transparency obligations by allowing downstream systems to identify synthetic text, which is a key requirement in emerging AI regulations such as the EU AI Act.

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Encrypt all training data

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Limit model access to internal employees only

Question 5

A healthcare AI system uses patient data to predict disease risk. To comply with HIPAA and reduce the risk of re-identification, which technique should be applied to the training data before model development?

Accepted Answer

Differential privacy with a carefully chosen epsilon. Differential privacy (Option C) is the correct technique because it adds calibrated noise to the training data or model outputs, providing a mathematical guarantee against re-identification even if an attacker has auxiliary information. This directly addresses HIPAA's requirement to protect patient privacy while preserving statistical utility for disease risk prediction.

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Pseudonymisation by replacing patient names with random IDs

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Data augmentation to create synthetic samples

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Data minimisation by removing all features except age and gender

Question 6

An AI team is developing a model that approves loan applications. The dataset contains historical loan decisions where a protected group was disproportionately denied loans. The team wants to ensure the model does not perpetuate this bias. Which fairness metric should be used during validation to directly measure whether the model's positive prediction rate is equal across groups?

Accepted Answer

Demographic parity. Demographic parity requires the probability of a positive prediction (loan approval) to be equal across groups. This directly addresses the concern of perpetuating historical denial rates. Equalised odds measures error rates, not positive prediction rates.

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Calibration

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Individual fairness

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Equalised odds

Question 7

A company is deploying an AI system that screens job applications. According to the EU AI Act, this system is likely classified as high-risk because it affects employment opportunities. Which requirement must the company implement for high-risk AI systems?

Accepted Answer

A human-in-the-loop mechanism that enables override of the AI's decisions. The EU AI Act requires human oversight for high-risk AI systems to allow operators to override or stop the system's decisions when necessary. The other options are not mandated by the Act for high-risk systems.

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Full transparency by publishing the model's source code and training data

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Annual third-party audits of the model's energy consumption

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Obtaining explicit consent from each applicant to process their data

Question 8

A financial institution needs to deploy a credit scoring model that is interpretable to regulators. The model must provide clear reasons for each decision. Which model type should the institution choose?

Accepted Answer

A glass-box model such as logistic regression or a decision tree. Glass-box models like logistic regression or decision trees are inherently interpretable and can provide clear, auditable reasons for each prediction. Black-box models require post-hoc explainability methods, which may not be sufficient for regulatory scrutiny.

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A gradient-boosted tree ensemble with SHAP explanations

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A black-box model with a model card describing its behavior

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A deep neural network with LIME explanations

Question 9

A company is training a large language model from scratch and wants to minimise its environmental impact. Which practice aligns with green AI principles?

Accepted Answer

Use model pruning and train on a smaller, representative dataset. Green AI advocates for resource-efficient AI, including using smaller models, pruning, and efficient architectures to reduce carbon footprint. Training larger models with more data increases environmental impact, not reduces it.

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Use more GPUs to parallelise training and reduce wall-clock time

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Deploy the model on a cloud provider with renewable energy certificates

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Train the model on a larger dataset to improve accuracy

Question 10

An organisation is developing an AI policy. According to the NIST AI RMF, which function involves establishing policies and procedures to ensure the organisation governs AI responsibly?

Accepted Answer

Govern. The NIST AI RMF's Govern function focuses on establishing governance structures, policies, and accountability mechanisms. Map, Measure, and Manage are other functions in the framework.

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Manage

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Measure

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Map

Question 11

A company uses AI to generate marketing images. They want to ensure that the images are clearly identified as AI-generated to comply with transparency obligations. Which approach is most effective?

Accepted Answer

Embed a visible watermark stating 'AI-generated' in each image. Watermarking AI-generated content with visible or invisible markers is a direct way to disclose AI origin. Using metadata alone can be stripped; disclaimers in terms of service are not visible to users; relying on human detection is unreliable.

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Add a disclaimer in the platform's terms of service

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Include metadata in the image file indicating it is AI-generated

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Rely on deepfake detection algorithms to flag the images

Question 12

A hospital wants to train a diagnostic model using data from multiple hospitals without sharing raw patient data. Which technique allows model training across decentralised data while preserving privacy?

Accepted Answer

Federated learning. Federated learning trains a shared model by aggregating updates from local data without moving the data itself, preserving privacy. Differential privacy adds noise but doesn't decentralise data. Data centralisation violates privacy. Anonymisation alone doesn't allow collaborative training.

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Differential privacy applied to the combined dataset

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Centralising all data in one location and anonymising it

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Using pseudonymisation and then pooling the data

Question 13

Under the GDPR, individuals have the right to not be subject to a decision based solely on automated processing if it produces legal effects. Which of the following is a typical safeguard that organisations must provide to comply with this right?

Accepted Answer

The right to obtain human intervention on the part of the controller. GDPR requires human intervention as a safeguard for automated decisions with legal effects. The right to explanation and human oversight are key. Deleting data is not a safeguard; consent is not sufficient; detailed explanations are part of transparency.

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The right to have all personal data deleted immediately

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The right to receive a detailed mathematical explanation of the model

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The right to demand a more favourable automated decision

Question 14

A company is evaluating a vendor's AI system for hiring. The vendor claims the system is fair because it achieves demographic parity. However, the company discovers that the system has significantly different false positive rates across groups. Which fairness issue does this indicate?

Accepted Answer

The system violates equalised odds. Equalised odds requires that a model's false positive rates and true positive rates are equal across all demographic groups. Since the vendor's system has significantly different false positive rates across groups, it violates the equalised odds fairness criterion, even if demographic parity (equal selection rates) is satisfied. This is a core fairness metric in AI governance, as it ensures that errors are distributed equitably.

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The system violates individual fairness

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The system suffers from selection bias

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The system is not calibrated

Question 15

An AI system trained on historical medical records shows that certain racial groups have higher predicted risk for a disease. The data reflects real-world differences in diagnosis rates due to unequal access to healthcare. Which type of bias is this?

Accepted Answer

Historical bias. Historical bias occurs when the training data reflects past societal inequities. Selection bias would occur if the sample is not representative; confirmation bias stems from the model's own predictions; algorithmic bias arises from the model design.

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Algorithmic bias

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Selection bias

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Confirmation bias