AAISM Practice Test 8 — 10 Questions

Question 1

An e-commerce company discovers that its recommendation model, trained on historical customer data, systematically under-recommends products to users from certain demographic groups, reducing their access to promotional offers. The legal team flags potential fair-lending-style exposure even though no credit is involved. Which action BEST addresses this risk within an AI risk management framework?

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

Accepted Answer

Conduct a fairness assessment across demographic groups, document the disparity and its potential harms, then apply mitigation such as reweighting or post-processing and monitor outcomes continuously.. Addressing algorithmic disparity requires measuring outcomes across groups, documenting the disparity and its harms, applying targeted mitigation, and monitoring continuously. This mirrors the standard risk treatment cycle and produces auditable evidence of due diligence. Attribute removal fails because proxies persist, shutdown is disproportionate and unverifiable, and disclaimers change nothing about the outcome, so none of those options genuinely treat the risk.

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Add a disclaimer to the website stating that recommendations are automated and may vary, transferring responsibility to the user.

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Remove all demographic attributes from the training data and retrain, assuming that will eliminate the disparate impact.

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Disable the recommendation model for all users and revert to a static, non-personalized product listing until the issue is resolved.

Question 2

A financial services firm is building an AI risk taxonomy for its enterprise risk management framework. The CISO asks which characteristics distinguish AI-specific risks from traditional IT risks so that appropriate controls can be designed. Which TWO characteristics should be included? (Choose two.)

Accepted Answer

AI risk exposure changes continuously as models learn from or are exposed to new data, requiring ongoing rather than point-in-time assessment.. Two characteristics genuinely distinguish AI risk from traditional IT risk: emergent behavior that defies component-level prediction, and continuous change that invalidates point-in-time assessments. Together they justify ongoing monitoring, staged deployment, red-teaming, and version governance. The other characteristics either describe similarities, restate legal misconceptions, or falsely claim determinism, none of which belong in a defensible AI risk taxonomy.

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AI systems are exempt from data protection regulations because their outputs are statistical rather than factual.

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AI models are subject to the same patch management and vulnerability disclosure cycles as conventional enterprise software.

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AI models always produce deterministic outputs, allowing identical risk treatment to traditional deterministic business logic.

Question 3

Which TWO strategies are recommended for 'Human-in-the-Loop' governance?

Accepted Answer

Standardizing workflows for human intervention.. Option C is correct because standardizing workflows for human intervention ensures that Human-in-the-Loop governance is applied consistently, repeatably, and auditably across processes rather than relying on ad hoc judgment. Option E is correct because defining clear decision-making thresholds specifies exactly when a human must review, approve, or escalate an AI-generated decision, which is the core mechanism that keeps humans meaningfully in control. Together, these strategies establish both the process structure and the trigger criteria needed for effective human oversight. Option A is incorrect because using only AI for all HR decisions eliminates the human element entirely, which contradicts the Human-in-the-Loop principle. Option B is incorrect because allowing overrides without logging destroys the audit trail and accountability that governance requires. Option D is incorrect because removing humans to save money is the opposite of Human-in-the-Loop governance and removes necessary oversight.

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Using only AI for all HR decisions.

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Allowing humans to override without logging.

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Removing humans to save money.

Question 4

To mitigate adversarial evasion attacks against a vision model, you decide to implement defensive distillation on your TensorFlow model. What is the primary operational trade-off you must evaluate?

Accepted Answer

Reduced model sensitivity to noise and potential drop in clean accuracy. Defensive distillation trains a student model on soft labels produced by a teacher, smoothing the decision surface and reducing sensitivity to small adversarial perturbations. The trade-off is that this smoothing can lower accuracy on clean, unperturbed inputs because the model becomes less confident and more regularized. Practitioners must measure this drop before deploying.

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Requirement for GPU-based training clusters only

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Incompatibility with gradient-based optimizers

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Increased latency in inference requests

Question 5

When assessing the risk of 'Prompt Leaking', what is the most important control to implement?

Accepted Answer

Strict output filtering and instruction separation.. Prompt leaking occurs when an attacker extracts the system prompt or confidential instructions embedded in an LLM application. The most effective control is strict output filtering combined with instruction separation — keeping system instructions isolated from user input and filtering responses that would reveal them. This directly addresses the exfiltration channel rather than the model's size, transport, or network location.

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Increasing the model's parameter count.

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Encrypting the model output.

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Only allowing access from internal IP addresses.

Question 6

A healthcare organization is deploying a machine learning model to predict patient readmission risk. The model was trained on historical data that includes a 'zip code' feature, which strongly correlates with race. To comply with ISACA's AI governance principles and reduce bias, which technique should be applied during model development?

Accepted Answer

Apply adversarial debiasing during training to minimize the model's ability to predict the protected attribute.. Adversarial debiasing is a fairness-aware training technique that actively reduces a model's reliance on protected attributes by pitting the primary task against an adversary predicting those attributes. This addresses bias at its source during development, which is more effective than simply removing features or only measuring disparities. It aligns with ISACA's call for proactive controls to ensure ethical AI outcomes.

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Implement differential privacy by adding noise to the training data.

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Use a holdout test set to evaluate model performance across demographic groups.

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Remove the 'zip code' feature from the training dataset and retrain the model.

Question 7

A retail company's board has approved an AI governance charter. The Chief AI Officer must now establish a mechanism to ensure that AI risks are escalated to the board when they exceed defined thresholds. Which element is most essential for this escalation mechanism to function effectively?

Accepted Answer

A defined AI risk appetite statement with quantified tolerance thresholds. A defined AI risk appetite statement with quantified tolerance thresholds is the essential foundation for board escalation because it establishes objective criteria for when a risk exceeds acceptable levels. Without agreed tolerances, escalation decisions are subjective and inconsistent, undermining the governance charter's intent.

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An employee suggestion box for AI-related concerns

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A monthly newsletter summarizing AI industry trends

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A list of all AI vendors currently under contract

Question 8

A healthcare insurer is standing up an AI governance program and must decide which structural components to include in its governance framework to ensure AI systems are developed and operated consistently across business units. (Choose two.)

Accepted Answer

A defined governance body with assigned decision rights for AI approvals, exceptions, and oversight. A functioning AI governance framework rests on two structural pillars: a documented policy framework that sets principles, risk appetite, and mandatory controls, and a governance body with assigned decision rights for approvals, exceptions, and oversight. Together they ensure consistent treatment of AI across units, while controls and communications support execution.

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A vendor contract template requiring AI suppliers to disclose training data sources

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A monthly newsletter summarizing emerging AI regulations for interested employees

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A data loss prevention tool configured to block outbound transfers of model weights

Question 9

An AI platform team must let data scientists run training jobs that access a sensitive feature store. Auditors require that no long-lived credentials exist in notebooks and that every access be attributable to a specific scientist. Which approach best satisfies both requirements?

Accepted Answer

Bind each scientist's workload to a federated identity that exchanges short-lived tokens for scoped access to the feature store.. The two requirements are eliminating long-lived secrets and preserving per-user attribution. Federating each scientist's identity and exchanging it for short-lived scoped tokens satisfies both, because credentials are ephemeral and every access carries the originating individual. Shared service accounts, a single platform key, and node-level roles all obscure or eliminate individual attribution while leaving standing or overly broad access in place.

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Store a single platform API key in a managed secrets vault and inject it into every training job at runtime.

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Grant the training cluster's node role broad read access to the feature store and rely on network segmentation to restrict callers.

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Issue each scientist a shared service account whose password is rotated every 90 days and stored in the notebook environment file.

Question 10

When reviewing an AI architecture, what does the 'Attack Surface' refer to?

Accepted Answer

The set of points where an attacker can interact with the system. In AI architecture, the 'Attack Surface' refers to the set of points where an attacker can interact with the system, including inputs, outputs, APIs, and interfaces. This encompasses all potential entry points that could be exploited to compromise the model's integrity, confidentiality, or availability. Understanding the attack surface is crucial for threat modeling and implementing appropriate security controls.

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The speed of the model's inference

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The number of users allowed to access the system

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The memory usage of the model during training