AAISM Practice Questions 2 — 50 Questions

Question 1

A healthcare insurer deploys an AI model to prioritize prior-authorization requests. During post-deployment monitoring, the AI governance lead discovers the model's denial rate for one demographic group is materially higher than for others, though overall accuracy meets the target. Which action should the governance lead take FIRST?

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

Accepted Answer

Document the finding, validate the disparity with a fairness assessment, and escalate through the AI risk escalation process for a remediation decision.. When monitoring reveals a material fairness concern, governance requires validation and escalation before remediation. The disparity may stem from data quality, proxy variables, or genuine model bias, and only a structured assessment can determine the appropriate response. Escalating through the defined risk process ensures legal, compliance, and clinical stakeholders make an informed, documented decision rather than reacting prematurely.

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Adjust the model's decision threshold until denial rates are equal across all demographic groups.

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Take no action because overall accuracy meets the target and the disparity may reflect legitimate clinical differences.

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Immediately retire the model and revert to fully manual review for all prior-authorization requests.

Question 2

A financial services firm uses a third-party large language model via API for customer-facing investment guidance. The AI risk manager is told that the vendor has updated the model weights without changing the API version number or notifying customers. The firm's own testing now shows different outputs for the same regulated prompts. Which risk management control failure does this scenario MOST directly illustrate?

Accepted Answer

Inadequate third-party AI supply chain transparency and change notification requirements in the vendor contract.. When an organization consumes a third-party model, its risk posture depends on the provider's change management practices. Silent weight updates invalidate prior validation, testing, and regulatory attestations. The most direct control failure is the absence of supply chain transparency: contracts must require advance notice of material model changes, and technical measures such as version pinning or pinned endpoints must ensure the organization can detect and revalidate changes before they affect production.

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Insufficient adversarial robustness testing against prompt injection attacks on the API endpoint.

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Failure to implement differential privacy on the prompts sent to the third-party model.

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Lack of model explainability tooling to interpret why outputs changed after the update.

Question 3

A global manufacturer is standing up an AI governance committee. The CISO wants to ensure the committee can make defensible decisions about high-risk AI deployments. Which TWO responsibilities are MOST essential for this committee to own directly? (Choose two.)

Accepted Answer

Resolving escalated AI risk issues and directing remediation when monitoring or audit identifies unacceptable risk.. A governance committee's essential authorities are approving high-risk use cases against defined criteria and resolving escalated risk issues with documented decisions. These responsibilities ensure accountability, consistency with risk appetite, and auditability. Implementation, daily monitoring, and contract negotiation are execution activities that belong to engineering, operations, and procurement, and assigning them to the committee would undermine its oversight independence.

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Performing daily operational monitoring of model drift and data pipeline health for every deployed model.

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Negotiating pricing and service-level agreements with third-party AI model providers.

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Writing and maintaining the production code for all AI models used in manufacturing operations.

Question 4

A software company's AI governance team is defining metrics to report to the board quarterly. The board wants assurance that AI risks are being managed, not just a list of models deployed. Which metric set BEST supports that objective?

Accepted Answer

Percentage of high-risk AI systems with completed impact assessments, open high-severity findings and their aging, and incident trends by severity.. Board reporting should demonstrate control effectiveness against risk appetite, not just activity volume. Metrics such as impact assessment completion for high-risk systems, aging of high-severity findings, and incident trends by severity reveal whether controls are working and where exposure remains. They allow the board to challenge management, allocate attention, and confirm that AI risk is being managed rather than merely monitored.

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Number of AI vendors onboarded and average contract value of AI-related purchases.

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Total training hours delivered on AI ethics and employee satisfaction scores with AI tools.

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Count of AI models in production and total number of AI-related help desk tickets.

Question 5

An enterprise has deployed a third-party AI-powered hiring screening tool. The vendor recently updated its model, and the organization's HR team was not notified beforehand. The AI governance committee needs to ensure that such changes are managed consistently going forward. Which action should the committee take FIRST?

Accepted Answer

Add a contractual clause requiring advance notification and review of material model changes before deployment.. The governance committee must close the gap that allowed an unannounced vendor model change to affect hiring. A contractual requirement for advance notification and review creates a repeatable control, ensuring the organization can evaluate risks and performance before changes go live. This approach balances operational continuity with oversight, directly addressing the third-party change management deficiency without overreacting or neglecting the root cause.

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Immediately terminate the vendor relationship and build an in-house model.

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Require the vendor to provide the model's source code for internal review.

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Instruct HR to manually re-screen all candidates processed since the update.

Question 6

A multinational corporation is establishing an AI governance program. The board has asked the AI security manager to identify the essential components that must be in place to ensure the program can enforce accountability across business units. (Choose two.)

Accepted Answer

A defined AI governance structure with clear roles and responsibilities, including an accountable executive.. Enforcing accountability requires both a clear governance structure with assigned responsibility and a centralized inventory of AI models with risk assessments. The governance structure provides authority and decision rights, while the inventory provides the visibility needed to track and oversee AI use. Communication tools, technology mandates, and surveys may support the program but do not establish the enforcement mechanisms essential for accountability.

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A requirement that all AI projects use the same programming language and cloud provider.

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An annual employee satisfaction survey that includes questions about AI tools.

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A monthly newsletter highlighting AI success stories from each business unit.

Question 7

A company is developing an internal LLM-based tool. Which activity is required for 'Accountability'?

Accepted Answer

Maintaining a log of model development and decision-making.. Accountability in AI governance means being able to demonstrate who did what, when, and why across the model lifecycle. Maintaining a log of model development and decision-making (C) creates the evidentiary record that supports traceability, audit, incident investigation, and regulatory response. Without such records, an organization cannot prove that its AI was developed and operated responsibly, which is the essence of accountability.

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Deleting all documentation.

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Limiting access to the CEO.

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Outsourcing to a third party.

Question 8

A global retailer is deploying a generative AI assistant that summarizes customer service transcripts for human agents. The legal team is concerned about privacy risk from the assistant retaining or regurgitating personally identifiable information (PII) from previous conversations. Which TWO controls BEST reduce this risk? (Choose two.)

Accepted Answer

Add a PII detection and redaction layer on both the input transcripts and the generated summaries.. The privacy risk stems from the assistant retaining or retrieving PII across sessions. Session isolation with no cross-session memory prevents one customer's data from appearing in another's summary, while input and output redaction catches residual identifiers. Together they address both the retrieval path and the generated output, which are the two points where leakage occurs.

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Increase the model's temperature setting to reduce deterministic recall of training data.

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Require human agents to sign an acceptable use policy prohibiting them from viewing PII.

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Apply differential privacy during fine-tuning of the assistant on historical transcripts.

Question 9

A bank's AI governance committee is reviewing a new generative AI tool for credit memo summarization. The vendor provides SOC 2 Type II reports but refuses to disclose training data sources, citing trade secrets. The committee must decide whether to approve the tool for production use. Which action BEST aligns with ISACA's AI risk management principles?

Accepted Answer

Conduct a formal risk assessment documenting the data provenance gap, require contractual attestations, and approve a limited pilot with monitoring before full rollout.. The most defensible action combines formal risk assessment with compensating controls and a bounded pilot. ISACA guidance emphasizes that AI risk cannot always be eliminated but must be identified, evaluated, and managed with documented decisions. Contractual attestations and monitoring create accountability where transparency is limited, and a pilot limits potential harm while generating evidence for a full rollout decision.

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Reject the tool outright because the vendor's refusal to disclose training data sources makes risk assessment impossible.

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Approve the tool with standard data loss prevention controls, since SOC 2 Type II validates the vendor's security posture.

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Escalate the decision to the vendor's account executive to negotiate disclosure of training data sources as a condition of purchase.

Question 10

A manufacturing firm's AI governance lead is designing the enterprise AI policy hierarchy. The board has approved a high-level AI principles statement. Business units now need actionable rules for selecting and deploying vendors that supply AI-enabled equipment. Which artifact should the governance lead create next?

Accepted Answer

A detailed AI vendor risk assessment standard with mandatory evaluation criteria, evidence requirements, and approval gates for AI-enabled suppliers.. A policy hierarchy moves from principles to standards to procedures, and the gap described is the standard layer. A vendor risk assessment standard provides mandatory, auditable criteria and approval gates that business units can apply consistently. Revising principles, delivering training, or building a monitoring dashboard each fails to supply the actionable selection criteria the scenario requires.

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A quarterly vendor performance dashboard reporting incident counts for AI-enabled equipment already deployed across plants.

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A training deck explaining the board's AI principles so that procurement staff can interpret them when negotiating vendor contracts.

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A revised AI principles statement with additional clauses addressing vendor responsibility and ethical sourcing.

Question 11

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.)

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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.