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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Exam Price:	US$459 (members), US$599 (non-members)
Related Certifications:	CISSP CISM
Certificate Validity Period:	3 years
Exam Duration:	150 minutes
Real Exam Qty:	90
Available Languages:	English
Passing Score:	450 (out of 800)
Exam Format:	Scenario-based, Multiple-choice
Recommended Training:	ISACA Official AAISM Training
Exam Registration:	ISACA AAISM Registration
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Computer-based; available at authorized PSI testing centers or via remote proctoring
Pre Condition:	Hold an active CISM or CISSP certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

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AAISM시험내용 & AAISM퍼펙트 덤프자료

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ISACA AAISM 시험요강:

주제	소개
주제 1	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.
주제 2	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.
주제 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.

최신 ISACA AAISM Certification AAISM 무료샘플문제 (Q261-Q266):

질문 # 261
Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

A. Implementing access controls to protect the AI system from unauthorized use
B. Enhancing monitoring and detection of model failures and anomalies
C. Increasing the detail of AI solution backup and restoration processes
D. Testing the AI infrastructure failover mechanisms

정답：D

설명：
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.

질문 # 262
During red-team testing of an AI system used for lending decisions, which technique BEST simulates a data poisoning attack?

A. Inputting encrypted data
B. Adding noise to output predictions
C. Stealing model weights
D. Corrupting training datasets to manipulate outcomes

정답：D

설명：
AAISM defines data poisoning as intentional manipulation of the training data to influence model behavior or outputs. Corrupting training data (D) is the exact definition of this attack type.
Noise injection (A) is model degradation testing. Model theft (B) is exfiltration. Encrypted data (C) is irrelevant.
References: AAISM Study Guide - AI Threats; Data Poisoning Attacks.

질문 # 263
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?

A. Management is prepared to disclose AI system architecture to stakeholders
B. Risk exposure due to bias in AI outputs is kept within an acceptable range
C. Ethical standards are incorporated into security awareness programs
D. Responsibility is defined for legal actions related to AI regulatory requirements

정답：B

설명：
According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity.
Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Integrity of AI Outputs) AI Security Management Study Guide - Bias and Output Integrity Controls

질문 # 264
Which of the following is the MOST serious consequence of an AI system correctly guessing the personal information of individuals and drawing conclusions based on that information?

A. The exposure of personal information may result in litigation
B. The exposure of personal information may lead to a decline in public trust
C. The publicly available output of the model may include false or defamatory statements about individuals
D. The output may reveal information about individuals or groups without their knowledge

정답：D

설명：
The AAISM curriculum states that the most serious privacy concern occurs when AI systems infer and disclose sensitive personal or group information without the knowledge or consent of the individuals. This constitutes a direct breach of privacy rights and data protection principles, including those enshrined in GDPR and other global regulations. While litigation, reputational damage, or loss of trust are significant consequences, the unauthorized revelation of personal information through inference is classified as the most severe, because it directly undermines individual autonomy and confidentiality.

질문 # 265
When deriving statistical information from AI systems, which source of risk is MOST important to address?

A. Systemic bias in data sets
B. Incomplete outputs
C. Lack of data normalization
D. Presence of hallucinations

정답：A

설명：
AAISM emphasizes systemic or structural bias as a high-impact risk because biased data leads directly to discriminatory insights or decisions when used for analytics or reporting. This risk affects fairness, compliance, and organizational reputation.

질문 # 266
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AAISM시험내용: https://www.itdumpskr.com/AAISM-exam.html

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