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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • 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 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 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.

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q58-Q63):

58. Frage
Which of the following should be done FIRST when an attacker exfiltrates sensitive information from an AI model?

Antwort: C


59. Frage
Which of the following data management practices poses the GREATEST risk to the reliability of an AI model's correlations?

Antwort: D

Begründung:
While removing outliers can sometimes help with model performance, "Deleting outlier data values" arbitrarily poses the greatest risk to reliability because it can remove rare but legitimate data points that are critical for accurate pattern recognition. In domains like fraud detection or risk management, the outliers are the most important data. Distorting the natural distribution of data can lead to skewed correlations and a model that fails to recognize high-impact "tail" events.


60. Frage
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?

Antwort: A

Begründung:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the " Lack of transparency in the algorithm " creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a " Black Box, " it may consolidate decision-making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.


61. Frage
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

Antwort: B

Begründung:
Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.
"Precision is the key metric when false positives have a significant operational cost. It provides insight into the model's ability to avoid incorrect positive classifications." Accuracy and recall give broader insights, but only precision directly measures false positive risk.
Completeness is not a standard ML metric.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Performance Metrics for Classification Models"


62. Frage
An IS auditor for a veterinary clinic was informed that the dog breed categorical variable is necessary for the predictive model. Which of the following introduces the MOST risk?

Antwort: C

Begründung:
Ordinal encoding assigns numeric values in an order (e.g., Poodle = 1, Labrador = 2, Husky = 3). This falsely implies hierarchy or ranking among breeds, introducing bias into the model.
AAIA emphasizes that incorrect encoding of categorical variables can cause:
* Artificially weighted importance
* Misleading correlations
* Incorrect predictions based on false orderFor non-ordered categories,one-hot encoding(D) is the correct, low-risk approach.Lack of scaling (A) is not directly relevant for categorical fields. Clustering (B) is unrelated.Thus, ordinal encoding introduces the highest risk.
References:
AAIA Domain 2: Data Encoding, Feature Engineering, and Bias Avoidance.


63. Frage
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