ISACA AAISM Prüfungsinformationen - AAISM Originale Fragen

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ISACA AAISM Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
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 AAISM Prüfungsinformationen <<

AAISM Übungstest: ISACA Advanced in AI Security Management (AAISM) Exam & AAISM Braindumps Prüfung

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ISACA Advanced in AI Security Management (AAISM) Exam AAISM Prüfungsfragen mit Lösungen (Q331-Q336):

331. Frage
Which of the following controls BEST mitigates the risk of data poisoning?

A. Digital watermarking
B. Data validation
C. Data set restoration
D. Intrusion detection

Antwort: B

Begründung:
The AAISM technical controls framework emphasizes data validation as the primary safeguard against data poisoning attacks. Poisoning occurs when attackers insert malicious or corrupted data into training sets. Validation techniques verify the quality, authenticity, and consistency of input data before training, preventing compromised samples from corrupting the model.
Restoration helps after compromise, watermarking protects ownership, and intrusion detection monitors networks rather than data quality. The most effective preventive measure is data validation.

332. Frage
Which of the following BEST describes an adversarial attack on an AI model?

A. Attacking the underlying hardware of the AI system
B. Conducting denial-of-service (DoS) attacks against AI APIs
C. Reverse engineering the AI model using social engineering techniques
D. Providing inputs that mislead the AI model into incorrect predictions

Antwort: D

Begründung:
In AAISM, an adversarial attack is defined by maliciously crafted inputs designed to cause erroneous model behavior (misclassification, targeted misprediction) while the model architecture and parameters remain unchanged.

333. Frage
An organization implementing a large language model (LLM) application notices significant and unexpected cost increases due to excessive computational resource usage. Which vulnerability is MOST likely in need of mitigation?

A. System prompt leakage
B. Sensitive information disclosure
C. Unbounded consumption
D. Excessive agency

Antwort: C

Begründung:
AAISM highlights unbounded consumption (token/payment exhaustion, unmetered tool calls, prompt bombs) as a key LLM risk affecting cost and availability. Controls include request quotas, max tokens, rate- limits, budget guards, circuit breakers, and cost-aware routing. Excessive agency (A) relates to unsupervised actions; sensitive disclosure (B) and prompt leakage (C) are confidentiality risks, not primary drivers of runaway compute spend.
References: AI Security Management (AAISM) Body of Knowledge - LLM Risk Taxonomy (Abuse & Cost Risks); Guardrails: Rate-Limiting, Quotas, and Budget Controls; Resilience and Cost-Containment Patterns.

334. Frage
Which of the following BEST describes the role of model cards in AI solutions?

A. They provide a standardized way to document the training data and AI model use cases
B. They help developers create synthetic data and train AI models
C. They are primarily used to visualize the performance of AI models
D. They are used to automatically fine-tune AI models by adjusting hyperparameters based on user feedback

Antwort: A

Begründung:
AAISM positions model cards as standardized documentation artifacts that record intended use and out-of-scope use, training/evaluation data characteristics, performance metrics across groups, limitations/risks, and governance controls/owners. Their purpose is transparency and assurance, not automated tuning or synthetic data generation. Visualization (A) may appear within a card, but the core role is structured documentation for governance, risk, and compliance.

335. Frage
The PRIMARY purpose of adopting and implementing AI architecture within an organizational AI program is to:

A. Deploy fast and cost-efficient AI systems
B. Align AI system components with business goals
C. Provide a basis for identifying threats and vulnerabilities
D. Ensure powerful and scalable AI systems

Antwort: B

Begründung:
AAISM describes AI architecture as a strategic alignment framework, ensuring AI systems, data pipelines, governance processes, and capabilities match organizational business goals and regulatory requirements.
While threat identification (B) and scalability (D) are advantages, the primary purpose is business alignment.
Option A is not a core architectural objective.
References: AAISM Study Guide - AI Architecture and Business Alignment.

336. Frage
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