ISACA AAIA學習筆記 & AAIA通過考試

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ISACA AAIA 考試大綱：

主題	簡介
主題 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.
主題 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.
主題 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 AAIA學習筆記 <<

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最新的 Advanced in AI Audit AAIA 免費考試真題 (Q23-Q28):

問題 #23
Which of the following provides the BEST evidence that an organization's AI integration is aligned with its overall technology strategy?

A. Key performance indicators (KPIs) have been defined for AI systems.
B. Management promotes a culture of innovation.
C. The IT department includes many individual contributors with AI expertise.
D. The organization has developed and socialized AI use policies.

答案：A

解題說明：
The BEST evidence of alignment between AI integration and technology strategy is that clear KPIs (A) have been defined for AI systems and tied to strategic objectives, measurable outcomes, and governance oversight. KPIs demonstrate that AI initiatives are monitored, performance is evaluated, and outcomes align with enterprise value.

問題 #24
An insurance company uses an AI model to set premium rates. To align with AI-related policies on fairness, which of the following is the FIRST course of action?

A. Allowing customers to contest premium rates provided by the AI model
B. Reviewing AI model training data to identify potential biases
C. Modifying the AI model's training dataset to address potential biases
D. Training alternate AI models and comparing biases with the primary model

答案：B

解題說明：
AAIA guidance states that fairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, the first course of action is to review training data (option B) to identify:
Skewed demographic distributions
Missing or underrepresented populations
Inappropriate use of sensitive attributes
Incorrect labels or improperly encoded variables
Historical decisions that may propagate discrimination

問題 #25
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?

A. Clustered data sets
B. Randomized data sets
C. Unlabeled data sets
D. Labeled data sets

答案：D

問題 #26
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

A. Group analysis
B. Adversarial testing
C. Confusion matrix
D. Latency testing

答案：C

解題說明：
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing. According to the AAIA™ Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made-false positives and false negatives-thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed-not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Evaluation Metrics"

問題 #27
An IS auditor is assessing the implementation of AI tools for evidence collection involving multiple data sources. Which of the following outcomes BEST indicates that AI-driven evidence collection has improved the audit process?

A. Ability to rely on unstructured data with minimal cleansing
B. Elimination of human judgment in data and evidence analysis
C. Extended reporting timelines that allow for AI model retraining
D. Reduced time spent gathering data with fewer errors in evidence compilation

答案：D

解題說明：
AI-driven evidence collection should enhance efficiency and accuracy. The BEST indicator of improvement is reduced time spent gathering data with fewer errors (B), showing that AI has streamlined data extraction, consolidation, and initial validation without compromising quality.
AAIA's content on AI in audit processes highlights benefits such as automation of repetitive tasks, improved coverage, and higher-quality evidence.

問題 #28
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