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[General] AAIA New Study Materials, Reasonable AAIA Exam Price

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【General】 AAIA New Study Materials, Reasonable AAIA Exam Price

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ISACA AAIA Exam Syllabus Topics:
TopicDetails
Topic 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.
Topic 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.
Topic 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.

ISACA Advanced in AI Audit Sample Questions (Q28-Q33):NEW QUESTION # 28
A bank uses a video-based know your customer (KYC) verification process. Cybercriminals exploit this process by using deepfake technology to impersonate bank customers. Which of the following countermeasures is the BEST way for the bank to mitigate this risk?
  • A. Encrypting all customer data and communication
  • B. Discontinuing the use of the video-based verification process
  • C. Leveraging AI-based liveness detection during video verification
  • D. Requesting additional identity and address documents for verification
Answer: C

NEW QUESTION # 29
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?
  • A. It reduces the need for human oversight, ensuring seamless and autonomous data governance.
  • B. It accelerates AI implementation timelines by fully automating data preparation processes.
  • C. It focuses on enhancing the accuracy and reliability of AI model predictions.
  • D. It fosters adherence to industry regulations while minimizing the risk of data breaches and privacy violations.
Answer: D
Explanation:
According to the AAIA™ Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust.
"A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle." While option A is an outcome of good data governance, and automation (B) may improve efficiency, the most fundamental benefit is risk reduction and compliance (C). Option D reflects a misunderstanding of governance which requires human oversight.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Governance Frameworks and Compliance"

NEW QUESTION # 30
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?
  • A. Specificity
  • B. Recall
  • C. Accuracy
  • D. Precision
Answer: B
Explanation:
In predictive maintenance use cases-such as detecting turbine failure-the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIA™ Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
"Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives." Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Evaluation Metrics and Predictive Accuracy"

NEW QUESTION # 31
During audit planning, an IS auditor reviews the correlation matrix. Which variable pair from an electrical generation facility has the MOST significant correlation?
  • A. Temperature and generator effectiveness is -0.85
  • B. Rotational speed and tool wear is 0.56
  • C. Electricity demand and machine torque is 0.0
  • D. Daily precipitation and generator efficiency is 0.09
Answer: A
Explanation:
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

NEW QUESTION # 32
Which of the following is MOST important for an IS auditor to consider when identifying AI risk in a know your customer (KYC) application within a banking organization?
  • A. Intellectual property leakage and invalidation
  • B. Business disruption and financial impact
  • C. Incident response plan
  • D. Benchmarking against peer organizations
Answer: B
Explanation:
In high-stakes financial applications like KYC, the primary concern is the potential business and regulatory impact of an AI error-such as false customer rejection or failure to detect fraudulent accounts. The AAIA™ Study Guide emphasizes aligning AI risk assessments with business impact and regulatory exposure.
"In financial institutions, the most material risk of AI errors lies in operational disruption and regulatory fines.
KYC models must be assessed for how errors can lead to compliance failures or reputational harm." Benchmarking (B) supports best practice alignment, and incident response (C) is part of mitigation, but D addresses the most critical consequence of AI risks in banking.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Risk Impact and Business Alignment in AI Systems"

NEW QUESTION # 33
......
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