AAISM Practice Test 7 — 10 Questions

Question 1

A hospital is deploying an AI triage system that recommends patient priority levels. The risk committee wants to ensure ongoing risk visibility after go-live. Which TWO activities provide continuous risk visibility for this deployed system? (Choose two.)

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

Accepted Answer

Monitoring model input distribution drift against the training baseline.. Continuous risk visibility after deployment requires monitoring activities that generate ongoing signals. Input distribution drift monitoring detects when live data diverges from training conditions, and tracking clinician override rates captures real-world disagreement that may indicate model errors or edge cases. Static artifacts such as validation reports, one-time audits, and architecture documentation do not provide continuous visibility.

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Documenting the model architecture and hyperparameters in the model registry.

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Conducting a one-time fairness audit before the system goes live.

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Reviewing the model's original validation accuracy report from the pre-deployment assessment.

Question 2

A telecommunications company operates AI systems in twelve countries. Its AI governance committee must design a control testing program that provides assurance to the board that AI controls are operating effectively. The committee wants testing that is independent of the teams building and running the models. Which approach best meets this objective?

Accepted Answer

Commission the internal audit function to test AI controls against the governance framework, report findings independently, and track remediation to closure.. Independent assurance requires a testing function separate from the teams building and operating AI systems. Internal audit provides that independence, tests controls against the governance framework, reports findings through its own channel, and tracks remediation. Self-assessment, committee document review, and developer-run pipeline checks all originate from parties with a stake in the outcome and therefore cannot deliver objective assurance to the board.

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Require each AI product team to complete a self-assessment questionnaire quarterly and aggregate the results into a board dashboard.

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Ask the AI governance committee to review control documentation submitted by product teams and certify effectiveness at its monthly meeting.

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Engage the model development teams to run automated control checks in their pipelines and publish the pass rates to the board each quarter.

Question 3

Which document outlines the 'Roles and Responsibilities' for AI security?

Accepted Answer

The AI Governance Charter.. The AI Governance Charter is the foundational document that defines the governance structure for AI, including who owns what — the roles and responsibilities of the AI governance board, model owners, data stewards, security teams, and business units. It establishes accountability, decision rights, and escalation paths. This makes it the authoritative source for 'Roles and Responsibilities' in AI security.

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The printer configuration manual.

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A software license file.

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The company holiday calendar.

Question 4

An AI security manager is assessing the risk of model inversion attacks on a machine learning model that predicts patient outcomes. The model is accessible via an API that returns confidence scores. Which TWO controls are MOST effective at reducing the risk of model inversion? (Choose two.)

Accepted Answer

Limit the API to return only the top-1 predicted class without confidence scores.. Model inversion attacks exploit the model's tendency to memorize training data, often using confidence scores to reconstruct sensitive information. Limiting API output to top-1 class removes the granular data attackers need, while differential privacy mathematically bounds the influence of any single training record, directly preventing memorization. These two controls together provide strong defense, whereas rate limiting, encryption, and smaller models do not adequately address the core risk.

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Encrypt the model's parameters at rest and in transit.

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Use a model architecture with fewer parameters to reduce its memorization capacity.

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Implement rate limiting on the API to restrict the number of queries per user per hour.

Question 5

A retail company's AI-powered recommendation engine begins suggesting products that are culturally insensitive in certain regions. The model has not been changed, but sales data from a newly acquired subsidiary was recently added to its training pipeline. Which risk management principle does this scenario BEST illustrate?

Accepted Answer

Model drift caused by changes in the data supply chain can introduce new ethical and reputational risks.. The scenario shows that integrating new data from an acquisition altered the recommendation engine's behavior, producing culturally insensitive outputs without any model code change. This is a data supply chain induced drift that creates ethical and reputational risk, underscoring the need to govern data provenance and monitor model behavior after data pipeline changes.

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Acquiring a subsidiary always requires retraining all enterprise AI models from scratch.

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Recommendation engines are inherently unbiased and only reflect objective customer preferences.

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Ethical risks in AI are only relevant during the initial design phase and not after deployment.

Question 6

An organization is developing an AI risk register as part of its AI governance program. The AI security manager needs to ensure that risks are tracked and managed throughout the AI system lifecycle. Which element is MOST critical to include in the risk register for each identified AI risk?

Accepted Answer

The risk owner, likelihood, impact, and current mitigation status.. An AI risk register must contain actionable information for managing each risk. The risk owner ensures accountability, while likelihood and impact allow prioritization. Mitigation status tracks progress in reducing the risk. These elements are essential for any risk management framework, including those aligned with NIST AI RMF or ISO/IEC 23894. Technical details like algorithm or parameter count are secondary.

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The name of the data scientist who developed the model.

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The number of parameters in the model.

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The specific machine learning algorithm used, such as random forest or neural network.

Question 7

A hospital network is deploying a clinical decision support model that recommends triage priorities. The risk committee wants to establish human oversight that satisfies AI risk management expectations while preserving clinical workflow efficiency. Which TWO design choices BEST achieve meaningful human oversight? (Choose two.)

Accepted Answer

Require the clinician to record an override reason whenever the recommendation is rejected, and route that record into periodic model review.. Effective human oversight requires that the reviewer has enough information to evaluate the recommendation and that their disagreement produces a usable signal. Presenting rationale and uncertainty supports informed judgment, while capturing override reasons converts clinical disagreement into evidence that feeds model review. Together these choices create an oversight control that is both substantive at the point of care and useful for ongoing risk management, rather than a procedural formality.

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Restrict clinician access to the model's inputs so that staff cannot second-guess the recommendation with incomplete information.

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Configure the model to auto-populate the triage decision and require the clinician to click a single confirmation button to accept it.

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Limit oversight to a monthly aggregate dashboard review by the quality committee after decisions have already been executed.

Question 8

An AI governance lead is mapping the organization's AI risk register to the NIST AI Risk Management Framework. A business unit asks why the 'Map' function must be completed before the 'Measure' function is fully executed for a new fraud-detection model. Which response BEST justifies the sequence?

Accepted Answer

Mapping identifies the model's context, purpose, and affected stakeholders, which establishes the baseline against which measurement metrics and tolerances are defined.. The Map function establishes context, purpose, dependencies, and affected stakeholders, and that context determines which risks are relevant and what metrics and tolerances are meaningful. Measurement without mapping yields data that cannot be tied to business impact or risk appetite. The framework's structure reflects this dependency, so completing mapping first ensures that subsequent measurement, monitoring, and treatment activities target the risks that actually apply to the model's deployment context.

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Mapping must precede measurement because the Measure function can only be applied to models that have already achieved full production maturity.

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Measurement requires production telemetry, and the Map function is the mechanism that deploys the monitoring agents into the inference pipeline.

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Mapping is a regulatory filing requirement that must be submitted to the supervisory authority before any quantitative testing may begin.

Question 9

A retail company's AI team wants to deploy a customer-facing chatbot powered by a large language model. The chief information security officer asks the AI risk manager to identify the MOST significant risk introduced by this deployment that does not exist with traditional rule-based chatbots.

Accepted Answer

The potential for the model to generate unpredictable, contextually inappropriate, or harmful responses that bypass predefined business rules.. The defining new risk of generative AI in customer-facing applications is non-deterministic output generation. Rule-based chatbots follow predefined paths, so their responses can be fully anticipated and controlled. Large language models generate responses probabilistically, creating the possibility of harmful, off-brand, or policy-violating outputs. This requires a new layer of controls including output moderation, prompt injection defenses, and escalation procedures.

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Increased infrastructure cost due to the computational requirements of large language models.

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The difficulty of integrating the chatbot with existing customer relationship management systems.

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The need to comply with data residency requirements when the model is hosted in a cloud environment.

Question 10

A retailer's AI system uses a third-party foundation model accessed through a vendor API to summarize customer support transcripts. During risk assessment, the team realizes the vendor may use submitted content to improve its own models, and the retailer cannot inspect the vendor's training data or security controls. Which approach BEST manages this third-party AI risk?

Accepted Answer

Negotiate contractual terms that prohibit training on submitted data, require breach notification and deletion rights, and then validate those commitments through vendor assessment.. When an organization cannot inspect a vendor's data handling or security controls, the practical treatment is to bind the vendor through contractual obligations and verify those obligations through assessment. Prohibiting training on submitted content, requiring breach notification and deletion rights, and validating the commitments provides enforceable protection and creates evidence for the risk register. Relying on certifications alone, assuming the data is harmless, or hastily self-hosting without capability does not address the identified exposure.

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Accept the risk because the transcripts contain no regulated personal data and the model output is only used internally.

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Rely on the vendor's published security certifications as sufficient evidence that the data handling risk is acceptable.

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Move the workload to a self-hosted open-weight model immediately, regardless of whether the organization has the operational capacity to run it.