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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 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.
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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q157-Q162):

NEW QUESTION # 157
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model ' s poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?

Answer: D

Explanation:
A common pitfall in AI performance is " aggregate accuracy, " which masks poor performance in specific demographics or subgroups. To detect these " subgroup regressions, " auditors should look for " comparative evaluations " where the model is tested separately for different languages. " Parity thresholds " establish the maximum allowable difference in performance between the majority group (English) and minority groups.
This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.


NEW QUESTION # 158
Which of the following is MOST important to perform when testing an AI model for security?

Answer: D

Explanation:
AI security testing must address both traditional IT vulnerabilities and AI-specific threats.
According to the AAIATM framework, "Vulnerability Assessments" are essential for identifying weaknesses in the AI pipeline, including insecure APIs, lack of input sanitization, and susceptibility to adversarial attacks. This assessment helps auditors determine if the system can resist exploitation, such as prompt injection or data poisoning.


NEW QUESTION # 159
Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and integrity for AI model training and deployment?

Answer: A

Explanation:
Assessing data lineage provides insight into the origin, flow, and transformation of data across its lifecycle, which is crucial for validating data governance. The AAIATM Study Guide states that data lineage is essential to ensure the accuracy, consistency, and trustworthiness of data used in training AI models.
"Traceability of data sources is a core tenet of effective data governance. Data lineage validation ensures data quality, prevents unauthorized modifications, and maintains auditability."


NEW QUESTION # 160
When auditing the transparency of an AI system, which of the following would be the MOST effective way to understand the model's decision-making process?

Answer: A

Explanation:
Transparency in AI systems is a key requirement to ensure trust, accountability, and ethical compliance.
According to the ISACA AAIA™ Study Guide under the "AI Governance and Risk Management" section, understanding the decision-making process of an AI system falls under the principle of explainability.
Explainability refers to the degree to which an observer can understand the internal mechanics of an AI system and the rationale behind its outputs.
"Reviewing the explainability of AI outputs allows auditors and stakeholders to determine whether model decisions are interpretable and justifiable. High transparency means stakeholders can trace how and why a decision was made." While algorithm complexity and computational cost are technical considerations, they do not directly facilitate the audit of decision-making transparency. Similarly, training data diversity is essential for bias reduction but does not explain how decisions are derived. Therefore, option D is the most aligned with auditing transparency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Transparency and Explainability"


NEW QUESTION # 161
Which role is BEST suited to define the implementation roadmaps for adopting AI solutions?

Answer: B

Explanation:
A steering committee is responsible for enterprise-wide strategic decisions, technology alignment, investment prioritization, and governance oversight.
AAIA states that AI implementation roadmaps must be driven by a group that can evaluate:
* Organizational strategy
* Technology readiness
* Budget allocations
* Risk appetite
* Regulatory obligations
* Cross-functional impacts
The risk committee (A) focuses only on risk, not implementation. Product management (C) has a narrow scope. Internal audit (D) evaluates controls, not roadmaps.
Thus, the steering committee is the appropriate authority.
References:
AAIA Domain 1: AI Governance Structures
AAIA Domain 4: Strategic Alignment and Oversight


NEW QUESTION # 162
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