AAISM Übungsmaterialien - AAISM Lernressourcen & AAISM Prüfungsfragen

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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Certificate Validity Period:	3 years
Real Exam Qty:	90
Available Languages:	Spanish, English
Passing Score:	450/800
Related Certifications:	ISACA Advanced in AI Security Management (AAISM)
Exam Duration:	150 minutes
Exam Price:	USD $399
Exam Format:	Scenario-based Questions, Multiple Choice
Sample Questions:	ISACA AAISM Sample Questions
Exam Way:	Online remote proctored exam or test center exam
Pre Condition:	Candidates must hold an active CISM or CISSP certification.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline

>> AAISM Tests <<

AAISM Musterprüfungsfragen & AAISM Fragenpool

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ISACA AAISM Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
Thema 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.

ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q100-Q105):

100. Frage
A clinic has deployed a new AI application that processes protected health information. An AI security consultant raised concerns about validating transparency and traceability. Which tool BEST provides the information needed to address these concerns?

A. Vulnerability scanning
B. Score card
C. Model card
D. Penetration testing

Antwort: C

Begründung:
Model cards document key details about an AI model, including its design, training data, performance, and limitations, providing the transparency and traceability needed to understand how the model operates and makes decisions.

101. Frage
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?

A. Evasion attack
B. Model inversion
C. Model exfiltration
D. Poisoning attack

Antwort: A

Begründung:
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.

102. Frage
When using AI as part of incident response, which of the following BEST ensures the automation aligns with regulatory and governance obligations?

A. Apply anomaly detection models to filter incoming threats and automate containment
B. Use deep learning models to autonomously classify all incidents
C. Implement a tiered automation strategy where severity ratings inform the need for human oversight
D. Train the AI incident response platform to mirror legacy response workflows and log containment

Antwort: C

Begründung:
AAISM prescribes risk-based, human-in-the-loop orchestration for safety-critical or regulated actions. A tiered automation strategy that gates autonomy by incident severity, data sensitivity, and regulatory requirements ensures accountability, auditability, and proportionality, satisfying governance obligations. Full autonomy (A) risks non-compliance; simply mirroring legacy workflows (B) may not meet current obligations; broad auto-containment (C) lacks necessary oversight controls.
References: AI Security Management (AAISM) Body of Knowledge - Governance of AI-Driven Security Automation; Human Oversight and Escalation; Risk-Based Orchestration. AAISM Study Guide - Incident Response with AI: Controls, Approvals, and Auditability.

103. Frage
Which of the following is the MOST important consideration when deciding how to compose an AI red team?

A. Time-to-market constraints
B. AI use cases
C. Compliance requirements
D. Resource availability

Antwort: B

Begründung:
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.
References:
AAISM Exam Content Outline - AI Risk Management (Red Teaming Considerations) AI Security Management Study Guide - Tailoring Adversarial Testing to Use Cases

104. Frage
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

A. Ensuring the model is trained on diverse data sources
B. Incorporating more features and data into model training
C. Using robust data validation techniques and anomaly detection
D. Increasing model complexity

Antwort: C

Begründung:
AAISM identifies robust data validation and anomaly detection on incoming training data as the primary defense against data poisoning. These controls detect corrupted, manipulated, or adversarial samples before they enter the training pipeline.
Diverse data (A) is helpful but not protective against poisoning. More complexity (B) does not mitigate poisoning and can worsen vulnerability. More features (D) increases attack surface.
References: AAISM Study Guide - AI Threats; Data Poisoning Mitigation through Validation & Anomaly Detection.

105. Frage
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