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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Thema 2
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Thema 3
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

>> AAIA Fragen Antworten <<

AAIA Studienmaterialien: ISACA Advanced in AI Audit - AAIA Torrent Prüfung & AAIA wirkliche Prüfung

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q176-Q181):

176. Frage
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?

Antwort: A

Begründung:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial "perturbations"--small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively "smooths" the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms.
Defensive distillation specifically targets the model's resilience to technical exploitation.


177. Frage
Which of the following is the BEST way to ensure data fed into an AI model aligns with business objectives?

Antwort: D

Begründung:
Documenting data input requirements (option C) ensures that all incoming data supports the business purpose, operational constraints, and intended use cases of the AI model.
AAIA highlights that aligning AI systems with business objectives starts withclear data specifications, including:
* Required fields and data formats
* Data quality thresholds
* Acceptable ranges and constraints
* Mandatory attributes
* Source system definitions
* Business rationale for each feature
Without documentation, data pipelines may ingest irrelevant, low-quality, or misaligned data, causing the model to drift away from business needs.
Normalization (A) improves preprocessing but does not ensure alignment.
Switching data sources (B) is premature without evaluating needs.
Defining new attributes (D) is secondary to documenting overall requirements.
References:
AAIA Domain 1: Business Alignment and Data Requirements
AAIA Domain 2: Input Specification Governance


178. Frage
Which of the following is the BEST way to ensure data fed into an AI model aligns with business objectives?

Antwort: D

Begründung:
Documenting data input requirements (option C) ensures that all incoming data supports the business purpose, operational constraints, and intended use cases of the AI model.
AAIA highlights that aligning AI systems with business objectives starts with clear data specifications, including:
Required fields and data formats
Data quality thresholds
Acceptable ranges and constraints
Mandatory attributes
Source system definitions
Business rationale for each feature
Without documentation, data pipelines may ingest irrelevant, low-quality, or misaligned data, causing the model to drift away from business needs.


179. Frage
Which of the following is the BEST way to ensure an AI model's outputs are effectively controlled for bias?

Antwort: B


180. Frage
An IS auditor is planning an audit covering a vendor-provided and supported AI model used by the organization to predict train track maintenance. Which of the following is the BEST approach for the IS auditor to test the model?

Antwort: D

Begründung:
When auditing vendor-provided (third-party) AI models, the organization often lacks access to the underlying source code, neural network architecture, or weights. In such cases, the AAIA™ manual recommends " Black Box Testing " as the most practical and effective approach. This involves testing the model based solely on its inputs and outputs to verify its predictive accuracy, fairness, and reliability without needing internal technical details. White box testing (Option B) and code reviews (Option D) are typically unfeasible due to proprietary restrictions. Black box testing allows the auditor to validate that the model meets business requirements and operates within acceptable risk tolerances as defined in the vendor agreement.


181. Frage
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