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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • 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 2
  • 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 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.

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AAIA PrüfungGuide, ISACA AAIA Zertifikat - ISACA Advanced in AI Audit

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q78-Q83):

78. Frage
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?

Antwort: A


79. Frage
Which of the following is MOST important for an IS auditor to review during an AI system audit in order to determine compliance with intellectual property and data rights?

Antwort: D


80. Frage
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?

Antwort: A

Begründung:
False positives in fraud detection AI systems often stem from poorly optimized hyperparameters.
Hyperparameters control aspects of the model's learning process such as the learning rate, decision thresholds, and complexity penalties. When these parameters are not tuned correctly, the model can become overly sensitive and flag normal behavior as suspicious, leading to customer complaints.
"Hyperparameter tuning is essential to balance sensitivity and specificity in AI models. Improper tuning can result in a high rate of false positives or negatives, particularly in systems like fraud detection that require nuanced pattern recognition."


81. Frage
An organization developed an AI model trained on its monthly data. Which of the following would be the BEST validation method to avoid data drift?

Antwort: A

Begründung:
When dealing with monthly or temporal data, standard random splits (Option D) or cross-validation (Option A) can cause " temporal leakage, " where the model inadvertently learns from future data to predict the past.
According to ISACA AAIA™ principles, " Time Series " validation is the most appropriate method for sequential data. It involves training the model on a specific period (e.g., months 1-10) and testing it on the subsequent period (e.g., month 11). This approach accurately reflects how the model will perform in production and is essential for detecting data drift, as it identifies when seasonal trends or long-term shifts in customer behavior cause the model ' s accuracy to degrade over time.


82. Frage
An AI audit reveals that a loan approval model has a significantly higher rejection rate for a specific demographic group. What should be management's PRIMARY response?

Antwort: D

Begründung:
A significantly higher rejection rate is a clear indicator of potential algorithmic discrimination.
Management's PRIMARY response should be to conduct a comprehensive bias analysis (C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.


83. Frage
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