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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies and Controls | 38% | - AI Assurance and Safety
|
| Topic 2: AI Risk Management | 31% | - AI Threats and Vulnerabilities
|
| Topic 3: AI Governance and Program Management | 31% | - AI Governance Foundations
|
>> AAISM Valid Test Questions <<
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NEW QUESTION # 206
Which of the following BEST ensures the integrity of data sets used to train AI models?
Answer: A
Explanation:
AAISM defines cryptographic tracking and verification as the best control for ensuring the integrity of training data. By applying hashing and verification methods, organizations can confirm that datasets remain unaltered and authentic throughout collection, storage, and processing. Collecting only necessary data, proper storage, or clear documentation all support governance and compliance, but they do not guarantee that the data has not been tampered with. Integrity is specifically ensured by cryptographic verification techniques.
References:
AAISM Exam Content Outline - AI Risk Management (Data Integrity and Protection) AI Security Management Study Guide - Cryptographic Controls for Dataset Integrity
NEW QUESTION # 207
An attacker is able to exploit the intended behavior of an AI system using a specific input or classes of inputs. Which type of attack is this an example of?
Answer: A
Explanation:
An evasion attack occurs when an attacker crafts specific inputs designed to exploit the AI system during operation, causing the model to produce incorrect or unintended behavior while still functioning within its deployed environment.
NEW QUESTION # 208
After implementing generative AI tools, an organization was notified of new critical compliance requirements aimed at verifying how AI is being used. Which of the following should be done FIRST?
Answer: A
Explanation:
Establishing a responsible use policy provides the foundational governance framework that defines how AI should be used in alignment with compliance requirements, enabling subsequent controls, reviews, and reporting to be properly structured.
NEW QUESTION # 209
Which of the following is the MOST important consideration when deciding how to compose an AI red team?
Answer: A
Explanation:
AAISM materials specify that the composition of an AI red team must be tailored to the organization's AI use cases. The purpose of red-teaming is to simulate realistic adversarial conditions aligned with the actual applications of AI. For example, testing a generative model requires different expertise than testing a fraud detection system. While resource availability, compliance requirements, and time-to-market pressures are practical considerations, they are secondary to aligning team expertise with use case scenarios. The most important factor is therefore the AI use cases themselves.
NEW QUESTION # 210
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?
Answer: D
Explanation:
Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO
/RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents.
References:AI Security Management (AAISM) Body of Knowledge: Business Continuity & Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines.
AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.
NEW QUESTION # 211
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