Valid AAISM Vce, AAISM PDF Cram Exam

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ISACA AAISM Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2
  • AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3
  • AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following involves documenting and monitoring the complete journey of data as it flows through an AI system?

Answer: A

Explanation:
Data lineage records and monitors the end-to-end journey of data-sources, movements, transformations, storage locations, uses, and dependencies-providing traceability, auditability, and accountability across the AI lifecycle. "Origin" is a single point (provenance), "transformation" is one step within the flow, and
"processing" is a general activity rather than a governance record of the entire path.
References: AI Security Management (AAISM) Body of Knowledge: Data Governance-Provenance and Lineage; AAISM Study Guide: Lineage Documentation, Traceability, and Audit Evidence.


NEW QUESTION # 92
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

Answer: D

Explanation:
The priority control in AI vendor due diligence is ensuring explicit disclosure of data handling: data flows, purpose limitation, retention/deletion, training vs. inference use, isolation controls, access paths, subcontractors, and storage/transfer boundaries. This disclosure is then tied to contractual commitments and measurable controls. A public policy (Option A) may be incomplete; a pre-procurement external audit (Option C) can be valuable but is not always feasible or targeted to your data use; legal discussions (Option D) are necessary for terms but must be grounded in clear, detailed data-use disclosures to be effective.
References:
AAISM Body of Knowledge: Third-Party AI Risk Management; Data Governance and Usage Controls; Contractual and Technical Safeguards for Vendor AI.
AAISM Study Guide: AI Procurement Due Diligence; Data-Use Transparency (Training vs. Fine-tuning vs.
Inference); Retention, Purpose Limitation, and Cross-Border Controls.


NEW QUESTION # 93
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

Answer: B

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 # 94
The PRIMARY goal of data poisoning attacks is to:

Answer: D

Explanation:
AAISM defines data poisoning as the insertion of malicious or corrupted data into training (or fine-tuning) pipelines to degrade or bias model behavior, thereby compromising output integrity in production. While poisoning occurs during development/training (C), its primary objective is the downstream integrity impact on predictions/outputs (D). Options A and B relate to confidentiality threats (e.g., inversion or leakage), not poisoning.
References:* AI Security Management (AAISM) Body of Knowledge: Model Integrity Threats-data poisoning aims and effects; supply-side data controls* AAISM Study Guide: Risk scenarios for poisoning; mitigations via data provenance checks, robust training, and anomaly detection


NEW QUESTION # 95
Which of the following would BEST help mitigate vulnerabilities associated with hidden triggers in generative AI models?

Answer: B

Explanation:
Hidden triggers are adversarial backdoors planted in AI models, activated only by specific inputs. The AAISM materials specify that the best mitigation is to use adversarial training, which deliberately exposes the model to potential trigger inputs during training so it can learn to neutralize or resist them. Retraining with diverse data reduces bias but does not address hidden triggers. Differential privacy is focused on privacy preservation, not adversarial resilience. Monitoring outputs can help with detection but is reactive rather than preventative. The proactive solution highlighted in the study guide is adversarial training.
References:
AAISM Exam Content Outline - AI Risk Management (Backdoors and Hidden Triggers) AI Security Management Study Guide - Adversarial Training as a Mitigation Control


NEW QUESTION # 96
......

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