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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit (AAIA) Exam
Exam Number:AAIA
Exam Duration:120 minutes
Certificate Validity Period:Not publicly specified (requires ongoing CPE maintenance after certification)
Related Certifications:CISA
CIA
CPA
Real Exam Qty:55
Exam Format:Computer-based, Multiple-choice, Remote proctoring or test center, Closed-book
Exam Price:USD 459 (member), USD 599 (non-member)
Passing Score:65%
Available Languages:English
Recommended Training:Official AAIA Training Course Providers
ISACA AI Audit Training Resources
Exam Registration:ISACA AAIA Certification Page
Sample Questions:ISACA AAIA Sample Questions
Exam Way:Computer-based exam delivered via PSI test centers or remote proctoring
Pre Condition:Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification
Official Syllabus URL:https://www.isaca.org/credentialing/aaia

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AAIA Praxisprüfung, AAIA Lernressourcen

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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
  • 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.
Thema 3
  • 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.

ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q86-Q91):

86. Frage
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?

Antwort: D


87. Frage
Which metric is MOST important to consider when reviewing the performance of a machine learning model in avoiding false positive results?

Antwort: B

Begründung:
Precisionmeasures the proportion of positive predictions that were actually correct. When avoidingfalse positivesis the priority (e.g., fraud accusations, security alerts, loan denials), precision is the critical metric because:
* High precision = very few false positives
* Low precision = many false positives
AAIA stresses using appropriate performance metrics depending on risk context.
Accuracy (B) can mask imbalances.
Recall (D) relates to false negatives.
F1 (C) balances precision and recall but does not emphasize false positives specifically.
References:
AAIA Domain 2: Model Performance Metrics and Risk-Based Evaluation


88. Frage
A healthcare organization uses patient data to train an AI model for early disease detection. Which of the following practices provides the BEST assurance that personal data is secure and its integrity is maintained?

Antwort: B

Begründung:
In healthcare AI applications, protecting patient data is critical. The AAIA™ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).
"Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset." While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy and Security of Sensitive AI Data"


89. Frage
Which of the following BEST describes the purpose of a "model validation" function that is independent from the model development team?

Antwort: D

Begründung:
Independent model validation mirrors the "second line of defense" concept in risk management -- separating those who build the model from those who challenge and approve it reduces conflict of interest and confirmation bias.


90. Frage
An IS auditor is auditing an organization's data governance framework. The primary objective is to provide assurance that data management practices are standardized to support a trustworthy AI system. Which of the following should be the auditor's MOST important consideration?

Antwort: B


91. Frage
......

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