AAISM Practice Test 25 — 15 Questions

Question 1

An enterprise is defining its 'AI System Inventory'. Which metadata attribute is most critical for assessing legal and regulatory risk?

Accepted Answer

Training data source and provenance.. Training data source and provenance is the metadata attribute most directly tied to legal and regulatory risk because it determines data rights, consent, licensing, copyright exposure, and privacy obligations. Regulators need to know where data came from, whether it was lawfully obtained, and whether it contains personal or protected information. Without provenance, an organization cannot demonstrate compliance with GDPR, CCPA, or IP law.

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Hardware acceleration type.

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Inference latency in milliseconds.

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Model training compute cost.

Question 2

An organization is evaluating 'Third-Party AI Models'. Which risk is most effectively addressed by a 'Model Evaluation Report'?

Accepted Answer

The risk of model unsuitability or unexpected behavior.. A Model Evaluation Report documents how a third-party AI model was tested for accuracy, robustness, bias, fairness, and behavior under edge cases, directly addressing the risk that the model is unsuitable for the intended use case or behaves unexpectedly in production. It provides evidence that the model's performance and limitations are understood before deployment, which is the core purpose of model evaluation in AI risk management.

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The risk of the vendor going bankrupt.

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The risk of poor UI/UX design.

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The risk of high API latency.

Question 3

A financial services firm is deploying a third-party large language model (LLM) to summarize internal investment research. The security team must implement controls to detect when the model is induced to ignore its system instructions and leak confidential context. Which two controls directly address this risk? (Choose two.)

Accepted Answer

Deploy an input/output guardrail that uses a secondary classifier to flag instruction-override patterns and anomalous data egress in prompts and completions.. Runtime guardrails that inspect prompts and completions, combined with strict separation of system instructions from untrusted user content, directly counter instruction-override and context-leakage attempts. The other controls address unrelated concerns such as output randomness, training-data privacy, or user authentication, none of which stop an attacker from manipulating the model's instruction hierarchy during inference.

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Apply differential privacy during fine-tuning of the third-party model to prevent memorization of training data.

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Require multi-factor authentication for all users who access the summarization application.

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Increase the model's temperature setting to make outputs less deterministic and harder for attackers to predict.

Question 4

A hospital's AI governance committee is assessing an AI diagnostic imaging model that was trained on data from a single urban medical center. The model will now be deployed across a network of rural clinics. The committee wants to quantify the risk that model performance will degrade in the new environment. Which risk management activity should be performed FIRST to address this concern?

Accepted Answer

Conduct a dataset distribution shift analysis comparing the training population demographics to the target clinic populations.. The first step in managing any AI risk is to identify and analyze it with evidence. Comparing the training data distribution against the target deployment population is the analytical activity that reveals whether and how much the model's inputs will differ, which is the root cause of performance degradation. Only after that quantification can the committee rationally choose among mitigations such as retraining, local validation, or restricting use.

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Document the model's intended use in the AI system inventory and assign a risk tier based on the vendor's claims.

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Immediately retrain the model on data collected from the rural clinics to eliminate any demographic differences.

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Deploy the model in shadow mode at the rural clinics and monitor for adverse events before making any assessment.

Question 5

What is the primary role of 'Model Versioning' in AI governance?

Accepted Answer

To ensure reproducibility and enable rollback of problematic versions.. Model versioning creates an immutable record of each model iteration — weights, hyperparameters, training data snapshot, and code — so that any deployed model can be reproduced exactly and rolled back if it misbehaves. In AI governance, this supports auditability, incident response, and regulatory compliance by providing traceability from prediction back to the exact model artifact. Without versioning, a problematic model cannot be reliably reverted or investigated.

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To automate user login.

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To maximize server storage usage.

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To hide model architecture from hackers.

Question 6

A board of directors requests a summary of AI risk exposure. Which metric provides the most relevant insight into AI security program maturity?

Accepted Answer

The percentage of AI models that have completed a formal governance and security certification.. The percentage of AI models that have completed formal governance and security certification is the most direct indicator of program maturity because it measures how much of the AI estate has been subjected to structured review. It reflects both coverage (how many models are in scope) and rigor (whether they passed certification). Board-level reporting needs this kind of governance coverage metric, not operational telemetry.

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The average response time of the AI chatbot interface.

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The total number of GPU hours utilized for model training.

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The total number of AI-related code commits in the CI/CD pipeline.

Question 7

A healthcare analytics team trains a diagnostic model on patient records and wants to verify that the model does not memorize individual training examples. They need a technique that measures privacy leakage by comparing model behavior on training versus non-training data. Which approach should they use?

Accepted Answer

Membership inference testing to estimate whether specific records were part of the training set.. Membership inference testing is designed to quantify whether an adversary can infer that a specific record was used in training, which is exactly the memorization risk described. The other techniques address explainability, adversarial robustness, or operational drift, none of which measure privacy leakage from training data.

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Data drift monitoring to detect distribution changes between training and production inputs.

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SHAP value analysis to explain which features contributed most to each prediction.

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Adversarial example generation to test the model's robustness to small input perturbations.

Question 8

You are auditing an AI system for compliance with the EU AI Act. Which control is mandatory for 'High-Risk' AI systems to ensure transparency for human oversight?

Accepted Answer

Human-in-the-loop design and logging. The EU AI Act classifies certain AI systems as 'High-Risk' (e.g., in critical infrastructure, employment, education, or law enforcement) and imposes mandatory requirements including human oversight, risk management, technical documentation, logging/traceability, and transparency. Human-in-the-loop design combined with logging ensures that a human can interpret outputs, intervene, and that decisions are auditable — directly satisfying the transparency and oversight obligations under Articles 13-14.

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Automated decision-making without oversight

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Mandatory anonymization of all input data

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Exclusive use of proprietary datasets

Question 9

An organization is establishing an AI governance program. The Chief Executive Officer asks the AI governance lead to define the program's scope. Which of the following should be the PRIMARY focus when defining the scope of AI governance?

Accepted Answer

All AI systems and processes across the organization, including those developed internally and procured from third parties.. The primary focus should be all AI systems across the organization, both internal and third-party, to ensure comprehensive risk management and consistent governance. Narrowing scope to customer-facing, internally developed, or PII-only systems creates blind spots and fails to address the full spectrum of AI risks.

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Only AI systems that are customer-facing, as they pose the highest reputational risk.

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Only AI systems developed by the internal data science team, since the organization has direct control over them.

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AI systems that use personally identifiable information (PII), as they are subject to privacy regulations.

Question 10

A retail company is standing up an AI governance program and must define how AI risk appetite will be expressed and operationalized across the enterprise. Which TWO elements are necessary to translate an executive AI risk appetite statement into day-to-day governance? (Choose two.)

Accepted Answer

A documented mapping of each AI risk category to an accountable owner and the control evidence required to demonstrate adherence.. Translating AI risk appetite into practice requires quantified tolerance thresholds tied to use-case tiers with escalation paths, plus a mapping of each risk category to an accountable owner and required control evidence. These two elements make appetite measurable, assignable, and auditable. Uniform review, sentiment surveys, and zero-tolerance declarations do not produce measurable limits or ownership and therefore cannot operationalize an appetite statement.

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A policy statement declaring that the enterprise has zero tolerance for any AI-related harm across all use cases.

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A requirement that every AI use case, regardless of risk, undergo the same full review by the central governance board.

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A quarterly survey of employees to gauge general comfort with AI and adjust the risk appetite based on sentiment.

Question 11

Which TWO activities ensure 'AI Safety' in development?

Accepted Answer

Adversarial testing (red teaming).. Option B, adversarial testing (red teaming), is correct because it proactively probes AI models for harmful, biased, or unsafe outputs by simulating malicious inputs, enabling developers to identify and remediate vulnerabilities before deployment. Option E, comprehensive documentation of safety guidelines, is correct because it establishes explicit policies, risk thresholds, and mitigation procedures that govern the AI lifecycle, ensuring accountability and consistent safety practices across teams. The remaining options do not ensure AI safety: unmonitored cloud servers (A) remove oversight and incident detection, increasing data exposure and misuse risk; simply increasing public data volume (C) can amplify bias, privacy violations, and unvetted content rather than improve safety; and removing all encryption (D) weakens data confidentiality and integrity, directly undermining security controls that support safe AI development.

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Using unmonitored cloud servers.

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Increasing the amount of public data used.

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Removing all encryption.

Question 12

You are configuring a 'Firewall' for an AI API. Which rule should you prioritize to prevent unauthorized exfiltration of model training data?

Accepted Answer

Configure strict egress (outbound) traffic rules. Strict egress (outbound) traffic rules control what data can leave the AI environment, directly preventing unauthorized exfiltration of training data or model artifacts. By defaulting to deny and allowing only approved destinations, ports, and protocols, you block covert channels an attacker might use to send stolen data out. This is the highest-priority firewall control for data exfiltration prevention.

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Disable all firewall logging

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Increase the number of API endpoints

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Allow all outbound traffic to the internet

Question 13

An organization is using Google Cloud Vertex AI and suspects an adversarial poisoning attack on a custom training dataset. Which tool should be used to verify the integrity and provenance of the training artifacts?

Accepted Answer

Vertex AI ML Metadata. Vertex AI ML Metadata tracks the lineage, provenance, and integrity of training artifacts, including datasets, models, and pipelines. It records metadata such as artifact versions, parameters, and execution details, enabling verification of whether a dataset was tampered with. This makes it the correct tool for investigating adversarial poisoning.

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Vertex AI Pipelines caching

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Cloud Data Loss Prevention (DLP) API

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Cloud Armor WAF

Question 14

When an AI model is suspected of being involved in a security incident, what is the first step in the incident response process?

Accepted Answer

Isolate the model endpoint from the network. Isolating the model endpoint from the network (C) is the containment step that stops an active incident from spreading, exfiltrating data, or continuing to serve manipulated outputs. In incident response, containment always precedes eradication, recovery, or any retraining activity, because you cannot safely investigate or remediate a system that is still reachable and live. Cutting network access preserves forensic state while halting ongoing harm.

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Retrain the model on new data

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Delete the model weights

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Update the documentation

Question 15

A machine learning model is showing signs of 'data drift'. What is the most effective initial step in the risk assessment process?

Accepted Answer

Analyze the statistical distribution of input production data versus training data.. Data drift means the statistical properties of production inputs have shifted away from the training distribution, so the first risk-assessment step is to quantify that shift. Analyzing the statistical distribution of production data versus training data (B) confirms whether drift is real, identifies which features drifted, and informs whether retraining, feature engineering, or monitoring changes are warranted. Acting before this analysis risks unnecessary or harmful interventions.

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Retrain the model immediately with all available data.

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Shut down the application to prevent incorrect predictions.

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Change the model architecture to a more complex design.