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ISACA AAIA Prüfungsplan:

Thema	Einzelheiten
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 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	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 (Q64-Q69):

64. Frage
During audit planning, an IS auditor reviews the correlation matrix. Which variable pair from an electrical generation facility has the MOST significant correlation?

A. Electricity demand and machine torque is 0.0
B. Temperature and generator effectiveness is -0.85
C. Daily precipitation and generator efficiency is 0.09
D. Rotational speed and tool wear is 0.56

Antwort: B

Begründung:
A correlation of -0.85 (option C) represents a strong negative correlation, indicating a meaningful inverse relationship. In AI model development, high-magnitude correlations (positive or negative) strongly influence feature selection and model behavior.
AAIA emphasizes that auditors must understand correlation strength to evaluate:
* Feature relevance
* Model weight justification
* Risk of multicollinearity
* Data quality and representativeness
The other correlations are weak or negligible.
Thus, the pair with -0.85 is the most significant.
References:
AAIA Domain 1: AI Data Relationships
AAIA Domain 2: Data Understanding and Statistical Foundations

65. Frage
Which control BEST mitigates the risk of a machine learning model being manipulated through adversarial input perturbations?

A. Adversarial robustness testing during model validation
B. Role-based access control on the training pipeline
C. Regular password rotation
D. Data encryption at rest

Antwort: A

Begründung:
Adversarial robustness testing deliberately crafts inputs designed to fool the model, allowing teams to measure and harden against evasion attacks before deployment.

66. Frage
Which of the following BEST describes the auditor's role when reviewing an organization's AI governance maturity?

A. Approving all new AI projects before development begins
B. Managing the organization's data science team
C. Independently assessing whether governance structures, policies, and controls are designed and operating effectively across the AI lifecycle
D. Building the AI models being audited

Antwort: C

Begründung:
The auditor's role is independent assurance -- evaluating whether governance, risk, and control structures exist and function effectively -- not operational involvement in building or approving AI systems.

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

A. F1 score
B. Precision
C. Accuracy
D. Recall

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

68. Frage
An IS auditor is testing an AI-based fraud detection system that flags suspicious transactions and finds that the system has a high false positive rate. Which of the following testing methods should be prioritized to BEST optimize the detection rate?

A. Substantive testing
B. Benford's Law analysis
C. Regression testing
D. Cross-validation testing

Antwort: D

Begründung:
Cross-validation testing is a statistical method used to assess how well a model generalizes to an independent data set. The AAIATM Study Guide recommends this method as a best practice to fine-tune model accuracy and reduce both false positives and false negatives. It involves splitting the dataset into training and testing subsets multiple times to ensure model robustness.
"Cross-validation allows auditors and developers to identify overfitting and adjust model parameters to achieve better generalization and predictive accuracy, especially in fraud detection contexts."

69. Frage
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