AI0-001 Practice Test 20 — 15 Questions

Question 1

A healthcare AI system diagnosing diabetic retinopathy from retinal images shows high accuracy overall but significantly lower recall for patients with darker skin tones. Which fairness metric would BEST capture this disparity by comparing true positive rates across groups?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Equalised odds. Equalised odds requires that the true positive rate and false positive rate be equal across groups. Demographic parity only checks outcome rates, not error types. Individual fairness compares similar individuals. Calibration checks confidence alignment.

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Calibration

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Demographic parity

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Individual fairness

Question 2

A data scientist is training a resume screening model to rank job applicants. The training data includes historical hiring decisions from the past 10 years. The company wants to avoid unfair bias against underrepresented groups. Which type of bias is most likely present in the training data?

Accepted Answer

Historical bias. Historical bias occurs when the training data reflects past societal biases, such as underrepresentation of certain groups in hiring. The model learns these patterns, perpetuating unfairness. Selection bias arises from non-random sampling, confirmation bias from favoring information that confirms preexisting beliefs, and algorithmic bias from model design choices.

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Algorithmic bias

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Selection bias

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Confirmation bias

Question 3

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 4

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.

Answer

Algorithmic bias

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Selection bias

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Confirmation bias

Question 5

A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?

Accepted Answer

Federated learning. Federated learning trains models across decentralised data without exchanging raw data. Differential privacy adds noise. Anonymisation removes identifiers. Pseudonymisation replaces identifiers with pseudonyms.

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Pseudonymisation

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Differential privacy

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Anonymisation

Question 6

A hospital uses an AI system to prioritize patient treatment. They want to ensure fairness across demographic groups. Which TWO fairness metrics should they apply to evaluate the model?

Accepted Answer

Equalized odds. Demographic parity requires equal selection rates across groups, and equalized odds ensures equal true positive and false positive rates, both relevant for medical triage.

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Individual fairness

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Accuracy parity

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Calibration

Question 7

An organisation is developing an AI system that will be used to evaluate teacher performance in public schools. To ensure responsible use, which TWO governance elements should be in place?

Accepted Answer

Develop a responsible use policy for the AI system. Responsible use policies and vendor AI assessment are key governance elements. Using only open-source models is not a governance requirement. Human oversight should be human-in-the-loop, not just monitoring. Publishing all training data may breach privacy.

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Implement human-on-the-loop oversight where humans only monitor outputs

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Publish the entire training dataset publicly for transparency

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Require the system to be built only with open-source models

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

An organisation is deploying an AI system for credit scoring, which is considered high-risk under the EU AI Act. Which requirement is NOT typically mandated for high-risk systems?

Accepted Answer

Publish the complete source code of the AI system. The EU AI Act requires risk management, data governance, transparency, human oversight, accuracy, and robustness for high-risk AI. However, it does not require publishing the full source code; only documentation and model cards may be required.

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Ensure training data is relevant and representative

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Establish a risk management system

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Provide human oversight mechanisms

Question 10

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 11

A healthcare AI model is subject to GDPR because it processes data of EU patients. The model makes automated decisions about treatment plans. Under GDPR, patients have the right to obtain an explanation of the decision. Which approach BEST satisfies this 'right to explanation'?

Accepted Answer

Generate a SHAP-based explanation showing the contribution of each input feature to the decision. GDPR's right to explanation requires meaningful information about the logic involved in automated decisions. Providing a clear, understandable explanation using techniques like SHAP fulfills this. A simplified summary may be needed, but the explanation must be substantive. Pointing to model cards or a confidence score alone is insufficient.

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Offer the patient the option to have the decision reviewed by a human

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State that the decision was made by an AI system and cannot be explained

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Provide a copy of the model's source code to the patient

Question 12

An organization wants to use a pre-trained language model from a third-party vendor. What is the most important security step before deployment?

Accepted Answer

Vet the model for backdoors and malicious behavior. Vetting the pre-trained model for backdoors or malicious behavior is critical to supply chain security. This may include scanning for anomalies, testing on specific inputs, and reviewing the model's origins.

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Host the model on a public cloud

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Apply differential privacy to the model

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Fine-tune the model on internal data

Question 13

An organization is deploying an LLM-based customer support agent. They want to protect against prompt injection attacks. Which THREE measures should they implement? (Select THREE.)

Accepted Answer

Rate limiting. Input sanitization removes malicious content from user input. Output filtering blocks harmful responses. Rate limiting reduces the ability to conduct automated attacks.

Answer

Increasing model temperature

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Disabling system prompts

Question 14

A healthcare AI system uses patient data to predict disease risk. To comply with privacy regulations, the organization wants to ensure that the model cannot reveal whether a specific patient's data was used in training. Which technique should they implement?

Accepted Answer

Differential privacy. Differential privacy adds noise to the training process, making it difficult to determine if any individual was in the training set. This directly addresses membership inference attacks.

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Homomorphic encryption

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Federated learning

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Model validation

Question 15

A data scientist is training a customer churn prediction model using sensitive customer data. To comply with data privacy regulations, they want to minimize the risk of membership inference attacks. Which TWO techniques should they consider?

Accepted Answer

Use techniques to reduce overfitting, such as regularization or simpler models. Differential privacy and reducing model complexity (e.g., limiting overfitting) are effective against membership inference. Data augmentation and cross-validation do not directly reduce inference risk. Using a black-box API is about deployment, not training.

Answer

Use k-fold cross-validation to improve model accuracy

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Deploy the model as a black-box API with no confidence scores

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Increase training data size through data augmentation