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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.

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ISACA Advanced in AI Audit Sample Questions (Q185-Q190):

NEW QUESTION # 185
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?

Answer: A

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 # 186
Which of the following techniques BEST supports machine learning (ML) training in sentiment analysis?

Answer: A

Explanation:
Sentiment analysis is a classification task where text is categorized (e.g., positive, negative, or neutral).
Logistic regression is a fundamental and highly effective statistical method for binary or multi-class classification, making it a standard choice for sentiment models. It estimates the probability of an input belonging to a specific class. While more complex architectures like Transformers (LLMs) are now common, logistic regression remains a benchmark for its simplicity and transparency in audit contexts. Image recognition and ANOVA are not relevant to the linguistic processing required for sentiment analysis, and the log-rank test is typically used in survival analysis rather than NLP classification.


NEW QUESTION # 187
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?

Answer: B

Explanation:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial "perturbations"--small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively "smooths" the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms.
Defensive distillation specifically targets the model's resilience to technical exploitation.


NEW QUESTION # 188
The PRIMARY objective of machine learning (ML) in data processing is to:

Answer: D


NEW QUESTION # 189
An organization utilizes an off-the-shelf generative AI solution for internal business processing. According to the principle of shared responsibility, which of the following areas remains the PRIMARY responsibility of the organization?

Answer: B

Explanation:
In a " Software as a Service " (SaaS) or off-the-shelf AI model deployment, the " Shared Responsibility Model " dictates that the vendor is responsible for the " Security OF the model " (infrastructure, base training, core safety), while the customer is responsible for the " Security IN the model " (usage). The organization ' s primary duty is " Defining usage policies and enforcing IAM " to ensure that only authorized employees use the tool and that they do not input sensitive corporate data into unvetted prompts. This governance ensures the AI is used ethically and securely within the organization's specific operational context.


NEW QUESTION # 190
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