AAISM Practice Test 4 — 25 Questions

Question 1

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?

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

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.

Answer

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 2

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.

Answer

An employee suggestion box for AI-related concerns

Answer

A monthly newsletter summarizing AI industry trends

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A list of all AI vendors currently under contract

Question 3

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.

Answer

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

Answer

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 4

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.

Answer

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 5

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.

Answer

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

Question 6

What is the primary role of 'Data Encryption at Rest' for AI datasets?

Accepted Answer

Protect data from unauthorized access on storage media. Data encryption at rest protects AI datasets by rendering stored data unreadable without the correct cryptographic keys, preventing unauthorized parties who gain access to storage media (stolen disks, misconfigured buckets, compromised backups) from reading the data. It is a confidentiality control, not a performance, detection, or patching mechanism.

Answer

Increase the training speed of the model

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Detect potential poisoning attacks

Answer

Automatically patch model software

Question 7

A financial services firm uses a third-party large language model (LLM) API to summarize internal audit reports. The CISO is concerned that confidential report content could be retained by the provider and later resurface in responses to other customers. Which risk management action BEST addresses this specific concern?

Accepted Answer

Require the provider to contractually commit to zero data retention and verify this through their SOC 2 Type II report and data processing addendum.. The core risk is that a third-party LLM provider may retain confidential audit content and expose it to other tenants. A contractual zero-retention commitment, backed by independent audit evidence and a data processing addendum, creates enforceable, verifiable assurance. Technical controls like prompt filtering or DLP address different threat vectors and would not prevent provider-side retention or cross-tenant leakage.

Answer

Deploy a data loss prevention (DLP) solution that blocks any audit report content from leaving the corporate network.

Answer

Fine-tune an open-source LLM on the audit reports and host it entirely on-premises.

Answer

Implement prompt injection filtering on all outbound requests to the LLM API.

Question 8

Which of these is a 'Proactive' risk management strategy?

Accepted Answer

Adversarial testing and threat modeling.. Adversarial testing and threat modeling are proactive strategies because they identify and mitigate potential risks before they materialize. They involve simulating attacks and analyzing system design to uncover vulnerabilities. This aligns with proactive risk management, which focuses on prevention rather than reaction.

Answer

Shutting down the system after an attack.

Answer

Incident response to a breach.

Answer

Adding more security after a leak is discovered.

Question 9

A global retailer is expanding its AI governance program to include third-party AI vendors. The Chief AI Officer wants to ensure that vendor-provided AI systems are subject to the same risk management rigor as internally developed models. Which TWO activities should be prioritized to integrate third-party AI into the governance framework? (Choose two.)

Accepted Answer

Conduct periodic security assessments of vendor AI systems, including penetration testing and review of their AI incident response plans.. Prioritizing vendor model documentation and periodic security assessments ensures transparency and ongoing risk management for third-party AI. Documentation reveals potential risks, while security assessments verify controls over time. Delegating responsibility, limiting usage, or mandating identical tools do not provide the necessary oversight and integration into the governance framework.

Answer

Require vendors to use the same AI development tools and platforms as the organization to ensure compatibility.

Answer

Delegate all AI risk management responsibilities to the vendor and rely on their internal governance processes.

Answer

Limit vendor AI usage to non-critical business functions to reduce potential impact.

Question 10

When evaluating AI security, 'Model Drift' refers to performance degradation over time. Which tool would a practitioner use to detect drift in a production environment?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is purpose-built to detect data drift, model quality drift, bias drift, and feature attribution drift in production endpoints. It captures inference data, compares it against baselines, and emits CloudWatch alarms when drift exceeds thresholds. This directly addresses the need to detect performance degradation over time in a deployed model.

Answer

AWS Config

Answer

Amazon GuardDuty

Answer

AWS CloudTrail

Question 11

What is the 'Membership Inference' attack in machine learning?

Accepted Answer

Determining if a specific record was in the training set. Membership inference determines whether a specific record was part of the model's training set by observing the model's outputs (e.g., confidence scores) for that record. It is a privacy attack because it can reveal sensitive information — for example, that a particular patient's data was used to train a medical model, which itself leaks health status. The attack exploits the tendency of models to be more confident on training data than on unseen data.

Answer

Brute forcing the user password

Answer

Adding noise to the output

Answer

Stealing the model's entire weights file

Question 12

When designing a secure AI development lifecycle (SDLC), which of the following is an essential governance activity during the 'Model Deployment' phase?

Accepted Answer

Finalizing the Model Card to document intended use, limitations, and security parameters.. The Model Card is the governance artifact that documents a model's intended use, limitations, performance characteristics, and security parameters, and finalizing it at deployment ensures downstream consumers and reviewers understand the model's boundaries and risks. It is an essential governance activity because it creates the accountability record required before a model goes live. Deployment gates typically require a completed model card as evidence of responsible release.

Answer

Running a brute-force attack on the model API.

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Updating the GPU drivers on the inference servers.

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Resetting the encryption keys for the production database.

Answer

Removing all training data from the production environment.

Question 13

Which THREE of the following are examples of AI system 'Access Controls'?

Accepted Answer

API Key authentication. API Key authentication (B) is an access control because it requires a caller to present a valid key before the AI service or endpoint will accept the request, authenticating and gating access. Role-Based Access Control (C) is a classic access control mechanism that grants permissions to AI resources and operations based on a user's assigned role, enforcing least privilege. Network Security Groups (D) act as access controls by allowing or denying inbound and outbound traffic to AI workloads based on rules, thereby restricting which sources can reach the system. Disabling logging services (A) is not an access control but rather the removal of an auditing/monitoring capability, and increasing model training epochs (E) is a model-training hyperparameter change that has no bearing on who or what may access the AI system.

Answer

Disabling logging services

Answer

Increasing model training epochs

Question 14

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?

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

Accepted Answer

Fine-tune the generative model on a curated dataset of clinically validated medical information and implement a guardrail that restricts responses to that knowledge base.. In healthcare, generative AI must be grounded in validated knowledge to prevent harmful hallucinations. Fine-tuning on curated clinical data combined with a retrieval guardrail that limits responses to that data ensures the chatbot provides accurate, safe information. Content filtering, disclaimers, or full human review are either ineffective, insufficient, or impractical for this use case.

Answer

Require all chatbot responses to be reviewed and approved by a licensed physician before being sent to the patient.

Answer

Implement a content filter that blocks any response containing medical terminology not approved by the organization's clinical team.

Answer

Deploy the chatbot in a limited pilot with a disclaimer that the information is not medical advice and monitor user feedback for harmful outputs.

Question 15

Which phase of the AI Lifecycle is most critical for 'Bias Detection'?

Accepted Answer

Model testing and evaluation.. Model testing and evaluation is the phase where bias metrics (e.g., disparate impact, equal opportunity difference) are computed against validation and test datasets across protected groups. This is where the model's actual behavior is measured and compared to fairness thresholds before deployment. Bias detection requires empirical evaluation of outputs, making testing/evaluation the critical phase.

Answer

Contract negotiation with vendors.

Answer

Model decommissioning.

Answer

Data center power-up.

Question 16

You are implementing 'AI Red Teaming'. What is the most effective way to test against prompt injection?

Accepted Answer

Execute a series of iterative adversarial prompt attempts against the production model.. Executing iterative adversarial prompt attempts against the production model is the most effective way to test for prompt injection because it directly exercises the attack surface — the model's input handling — under real conditions. Prompt injection is an input-layer vulnerability, so the only reliable test is to attempt injections and observe whether the model follows injected instructions, leaks system prompts, or bypasses guardrails. Iterative testing (red teaming) allows testers to refine payloads based on model responses.

Answer

Check for compliance with data privacy laws.

Answer

Review the training data logs for errors.

Answer

Scan the code repository for hardcoded secrets.

Question 17

Which THREE of the following are common steps in an 'AI Incident Response Plan'?

Accepted Answer

Containment of the affected AI model. Option E is correct because identification and classification of the AI incident is the essential first phase of any incident response plan, establishing what happened, its severity, and the appropriate response track before other actions are taken. Option A is correct because containment of the affected AI model limits blast radius by isolating, quarantining, or disabling the model (e.g., revoking API keys, taking the endpoint offline, or rolling back to a known-good version) so the incident does not spread or cause further harm. Option C is correct because eradication of the threat source removes the root cause — such as poisoned training data, a compromised model artifact, a malicious prompt-injection vector, or an exploited vulnerability — so the incident cannot recur after recovery. Option B is not a response step; increasing inference speed is a performance-tuning activity unrelated to incident handling. Option D is not a standard response step; wholesale hardware replacement is a disproportionate, costly action that is not part of a typical AI incident response plan.

Answer

Increasing the model's inference speed

Answer

Replacing all hardware in the data center

Question 18

Which TWO factors contribute to 'Model Obsolescence' risk?

Accepted Answer

Availability of superior, more efficient alternatives.. Model obsolescence risk arises when a deployed model no longer delivers optimal value relative to what is available or what the environment demands. Option A is correct because the emergence of superior, more efficient alternatives (e.g., newer architectures, better-performing pretrained models, or more cost-effective algorithms) can render an existing model outdated even if it still functions correctly. Option C is correct because significant changes in production data distribution — known as data drift or concept drift — cause the model's learned patterns to no longer match real-world inputs, degrading performance and effectively making the model obsolete for its original purpose. Option B is incorrect because reaching a maximum iteration count is a training hyperparameter/stopping condition, not a cause of obsolescence; the model simply stops training. Option D is incorrect because encrypting training data is a security measure and has no bearing on whether a model becomes obsolete. Option E is incorrect because storing a model on a local server is a deployment/hosting choice and does not by itself contribute to obsolescence risk.

Answer

The model has reached its maximum iteration count.

Answer

The training data was encrypted.

Answer

The model is stored on a local server.

Question 19

What is 'AI Shadowing' or 'Shadow AI' in an enterprise setting?

Accepted Answer

Employees using unapproved AI tools for work tasks.. Shadow AI refers to employees using unapproved AI tools — such as public LLM chatbots, browser extensions, or SaaS AI features — for work tasks without IT or security oversight. This creates data leakage, compliance, and intellectual property risks because sensitive information may be sent to third-party services outside the organization's governance.

Answer

The practice of monitoring AI usage in real-time.

Answer

The process of testing models before release.

Answer

The use of AI for internal cybersecurity tasks only.

Question 20

Which document should define the organization's stance on AI transparency and explainability?

Accepted Answer

The Corporate AI Policy.. The Corporate AI Policy is the governing document that defines the organization's principles, commitments, and requirements for AI transparency and explainability. It establishes the official stance that all AI systems must be documented, explainable to relevant stakeholders, and subject to disclosure rules. Other documents listed are either operational artifacts or external marketing materials that do not carry governance authority.

Answer

The employee breakroom notice.

Answer

The server configuration log.

Answer

The marketing brochure.