AAISM AI Governance And Program Management Practice Test 2 — 15 Questions

Question 1

When assessing a third-party AI provider, which document is most essential for due diligence?

Accepted Answer

A SOC 2 Type II audit report.. A SOC 2 Type II audit report is the most essential document for third-party AI due diligence because it independently validates the provider's security, availability, and confidentiality controls over time. It provides assurance that the provider meets industry standards. The other options lack the depth and independence required for risk assessment.

Answer

A public press release.

Answer

A standard company invoice.

Answer

The employee directory.

Question 2

An AI project is failing to meet its security requirements. Which governance process should be triggered to address this?

Accepted Answer

Risk acceptance or remediation plan initiation.. When an AI project fails to meet security requirements, the appropriate governance response is to initiate a formal risk acceptance or remediation plan. This ensures the risk is documented, evaluated against the organization's risk appetite, and either mitigated through a remediation roadmap or explicitly accepted by an accountable authority. This process maintains auditability and aligns with NIST AI RMF Manage and Govern functions.

Answer

Re-training the model without a review.

Answer

Immediate deletion of all data.

Answer

Ignoring the policy until the end of the year.

Question 3

Which mechanism best ensures 'Auditability' of AI decision-making?

Accepted Answer

Maintaining immutable logs of inputs, model versions, and outputs.. Auditability of AI decision-making requires a traceable record of what inputs were provided, which model version was used, and what outputs were produced. Maintaining immutable logs of these elements ensures that decisions can be reviewed, reproduced, and audited for compliance, bias, or errors. This is the foundational mechanism for AI auditability.

Answer

Deleting logs after 24 hours.

Answer

Using proprietary black-box models only.

Answer

Hardcoding all decisions.

Question 4

Who is ultimately responsible for the outcomes of an AI system deployed by a business unit?

Accepted Answer

The Business Unit Leader.. Ultimate accountability for an AI system's outcomes rests with the business unit leader who owns the system, its use case, and its business impact. Governance frameworks consistently assign ownership to the accountable executive rather than to vendors, auditors, or individual technical staff. The business unit leader has the authority, budget, and organizational standing to enforce controls and accept or mitigate risk.

Answer

The Cloud Service Provider.

Answer

The external auditor.

Answer

The junior data scientist.

Question 5

What is the benefit of 'AI Model Watermarking' for governance?

Accepted Answer

Facilitating identification and provenance of AI-generated content.. AI model watermarking embeds a detectable signal into model outputs or weights so that content can later be attributed to a specific model or owner. Its governance benefit is provenance: it enables identification of AI-generated content, supports authenticity verification, and helps enforce usage policies or detect misuse. Option B correctly captures this identification and provenance function.

Answer

Improving model prediction speed.

Answer

Preventing unauthorized hardware usage.

Answer

Reducing model memory footprint.

Question 6

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 7

An organization is using RAG (Retrieval-Augmented Generation). What is the primary governance concern for the retrieved documents?

Accepted Answer

Enforcement of document-level access controls.. In a RAG architecture, the retrieval layer pulls documents from external sources (vector stores, wikis, SharePoint, databases) and injects their content into the LLM's context window. The primary governance concern is ensuring that the retrieval mechanism enforces the same document-level access controls (ACLs) that apply in the source system, so a user cannot receive an answer synthesized from documents they are not authorized to read. Without this, RAG becomes a privilege-escalation and data-exfiltration channel that bypasses existing permissions.

Answer

The file size of the documents.

Answer

The font size used in the documents.

Answer

The language of the documents.

Question 8

When onboarding an AI startup vendor, what is the best strategy to manage 'Model Drift'?

Accepted Answer

Including performance and monitoring requirements in the SLA.. Model drift is an ongoing operational risk that manifests after deployment, so the most effective control is a contractual one that obligates the vendor to detect and remediate it. Embedding performance thresholds, drift-detection metrics, retraining cadence, and monitoring/reporting duties into the SLA creates enforceable accountability. This shifts drift management from a hope-based posture to a measurable, auditable service commitment.

Answer

Performing local code reviews.

Answer

Buying more computing power.

Answer

Trusting the vendor's promise of stability.

Question 9

A company is performing an 'AI Impact Assessment' (AIA). Which domain is the most critical for identifying 'Privacy' risks?

Accepted Answer

Data lineage, sensitivity, and processing purpose.. Privacy risk in an AI Impact Assessment is fundamentally about data: where it came from (lineage), how sensitive it is (PII, PHI, financial), and why it is being processed (purpose limitation). These three elements determine whether the processing complies with GDPR, CCPA, and similar regulations, and whether privacy harms (re-identification, inference, secondary use) are possible. Without this understanding, no meaningful privacy risk assessment can occur.

Answer

Model training costs.

Answer

UI design features.

Answer

Server CPU utilization.

Question 10

Which team should lead the definition of 'AI Acceptable Use' policies?

Accepted Answer

A cross-functional committee including legal, privacy, and security.. AI Acceptable Use policies span legal liability, data privacy regulation (GDPR, CCPA), security controls, and business ethics — no single department owns all of these domains. A cross-functional committee ensures the policy reflects regulatory obligations, privacy constraints, security requirements, and operational realities simultaneously. This mirrors how organizations govern other enterprise-wide policies like information security and data retention.

Answer

The marketing department.

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The software engineering team only.

Answer

The IT helpdesk team.

Question 11

Which activity is part of 'Ongoing AI Monitoring'?

Accepted Answer

Monitoring model performance and risk metrics.. Ongoing AI monitoring is the continuous, post-deployment phase where the organization tracks model performance (accuracy, drift, latency) and risk metrics (bias, fairness, security anomalies) to ensure the model remains safe and effective over time. Monitoring model performance and risk metrics is the defining activity of this phase. It detects degradation, data drift, and emerging threats before they cause harm.

Answer

Choosing the AI vendor.

Answer

Installing the server OS.

Answer

Initial software design review.

Question 12

Which scenario best illustrates an 'AI Security Incident'?

Accepted Answer

Unauthorized access to the model training weights.. An AI security incident is any event that compromises the confidentiality, integrity, or availability of AI assets — including model weights, training data, prompts, or inference endpoints. Unauthorized access to model training weights is a classic example because stolen weights enable model theft, adversarial fine-tuning, and intellectual property loss. The other options describe performance degradation, planned maintenance, or subjective dissatisfaction, none of which are security events.

Answer

The AI system running slower than usual.

Answer

A scheduled server maintenance window.

Answer

A user simply disliking the AI output.

Question 13

When evaluating AI 'Bias', which concept must be governed first?

Accepted Answer

Defining the organization's fairness and equity metrics.. Before bias can be evaluated or mitigated, the organization must define its fairness and equity metrics — the quantitative criteria that specify what constitutes fair treatment across groups. Without these definitions, bias assessment lacks a target and cannot be consistently governed.

Answer

Selecting the GPU brand.

Answer

Changing the model font.

Answer

Deleting the training database.

Question 14

What is the goal of an 'AI Security Awareness Program'?

Accepted Answer

To educate employees on AI security risks and safe usage.. An AI Security Awareness Program exists to educate employees about AI-specific security risks — such as prompt injection, data leakage, model misuse, and shadow AI — and to promote safe, policy-compliant usage. Its goal is behavioral and cultural change, not technical implementation or mandating AI adoption.

Answer

To write Python code for everyone.

Answer

To mandate AI usage in all tasks.

Answer

To replace human workers.

Question 15

Which control is most effective against 'Prompt Injection' attacks?

Accepted Answer

Using a secure AI Gateway with input filtering.. Prompt injection attacks manipulate an LLM by embedding malicious instructions in user input or external data, causing the model to execute unintended actions. A secure AI Gateway with input filtering inspects and sanitizes prompts before they reach the model, blocking known injection patterns (e.g., 'ignore previous instructions') and enforcing policy. This is a preventive, architectural control that directly addresses the attack vector, unlike the other options which are either irrelevant or impractical.

Answer

Adding more training data.

Answer

Requiring a longer password.

Answer

Disabling the internet.