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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 (Q180-Q185):

NEW QUESTION # 180
When using off-the-shelf AI models, which of the following is the MOST appropriate way for organizations to approach vendor management?

Answer: D

Explanation:
When organizations leverage off-the-shelf AI models, effective vendor management is critical to ensure operational reliability, compliance, and long-term support. The ISACA Advanced in AI Audit™ (AAIA™) Study Guide highlights that the "establishment of clear contractual terms regarding responsibilities for ongoing model updates, maintenance, support, and incident response is essential for managing third-party AI risks." By clearly defining the roles and expectations for updates and support (option B), organizations reduce the risk of unaddressed vulnerabilities, outdated models, or unclear recourse in the event of an incident or system failure. This approach supports ongoing risk management and ensures that both parties understand their obligations throughout the model's lifecycle.
While market research, vendor accreditation, and contract review by information security are important due diligence steps, they do not directly address the need for clarity in ongoing vendor responsibilities, which is critical for effective governance and sustained operation of AI solutions.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Vendor Management for AI Systems," Subsection: "Third-Party AI Risk and Contractual Obligations"


NEW QUESTION # 181
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

Answer: A

Explanation:
An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIA™ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.
"A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence." While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Evidence and Documentation of AI Strategy Alignment"


NEW QUESTION # 182
An IS auditor is considering the integration of AI techniques into the audit sampling process. Which of the following BEST enables the auditor to identify high-risk transactions within large data sets for targeted sampling?

Answer: C

Explanation:
Predictive analyticsis the most effective method for identifyinghigh-risk transactionsbecause it uses statistical models, anomaly detection, and machine learning to:
* Rank transactions by inherent and residual risk
* Detect hidden patterns that auditors cannot manually identify
* Highlight unusual transaction profiles, outliers, and red flags
* Prioritize transactions that require deeper inspection
AAIA's audit domain emphasizesrisk-based sampling enhanced by AI, where predictive models significantly improve coverage and accuracy.
NLP (A) extracts insights from text-not ideal for transaction risk scoring.
OCR (B) digitizes documents but does not identify risk.
Rule-based analytics (C) only catches known patterns; predictive analytics uncoversunknown or emerging risks.
References:
AAIA Domain 3: AI in Audit Processes(advanced analytics, anomaly detection, risk scoring).


NEW QUESTION # 183
Which of the following is the MOST important course of action for an organization prior to allowing end users to utilize an AI tool?

Answer: B


NEW QUESTION # 184
Which of the following is the GREATEST risk associated with using AI in audit planning?

Answer: A

Explanation:
In the context of AI-driven audit planning, the AAIA™ Study Guide identifies incomplete or inaccurate data as the most significant risk. If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.
"Audit planning supported by AI tools is only as strong as the underlying data. Incomplete data may cause incorrect risk assessments or misallocate audit resources, undermining audit effectiveness." While scope creep and cost are common concerns in project management, and limited AI knowledge can be mitigated through training, only incomplete data directly impacts the validity of audit planning outputs.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Data Integrity and Planning Risks"


NEW QUESTION # 185
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