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ISACA AAIA Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Governance and Risk33%- AI Risk Management
- Ethics, Regulations, and Standards for AI
- AI Models, Considerations, and Requirements
- Privacy and Data Governance Programs
- AI Governance and Program Management
Topic 2: AI Auditing Tools and Techniques21%- Audit Evidence Collection Techniques
- AI Audit Outputs and Reporting
- Audit Planning and Design
- Audit Testing and Sampling Methodologies
- Data Quality and Analytics for AI Audit
Topic 3: AI Operations46%- Operational Controls and Readiness
- Third-Party and Supply Chain Risk
- AI System Lifecycle and Deployment
- Performance Monitoring and Evaluation
- Incident Management and Resilience

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Updated ISACA AAIA Exam Questions And Answer

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

NEW QUESTION # 185
Which of the following is the GREATEST risk associated with using AI in audit planning?

Answer: D

Explanation:
In the context of AI-driven audit planning, the AAIATM 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 If the data input into AI tools is missing, outdated, or inconsistent, the model's suggestions for risk prioritization or control testing will be flawed.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.


NEW QUESTION # 186
Which of the following is the GREATEST challenge when auditing an AI-based decision-making system?

Answer: D

Explanation:
The " Black Box " nature of many modern AI systems, particularly deep neural networks, makes it difficult to understand exactly how a decision was derived. " Understanding the transparency (and explainability) of AI models " is identified by the AAIA™ manual as the greatest audit challenge because it limits the auditor ' s ability to verify fairness, detect hidden bias, or confirm compliance with logic-based regulations. Unlike traditional systems with deterministic code (Option A) or simple rules (Option B), AI decisions are probabilistic and non-linear. This lack of transparency requires auditors to use advanced explainable AI (XAI) tools to gain the necessary assurance over the decision-making process.


NEW QUESTION # 187
Which of the following initially provides assurance that the developer correctly interprets and identifies numerical data for balancing prior to inserting into the model?

Answer: A

Explanation:
A data dictionary (A) is the authoritative source for understanding:
Data types and numeric formats
Valid ranges and interpretations
Field definitions and business meaning
Normalization and scaling expectations
Before balancing or preprocessing data, developers must verify that they understand each feature correctly. The AAIA framework emphasizes that misinterpretation of numeric variables often leads to:
Incorrect normalization
Faulty scaling
Skewed class balancing
Inaccurate model training


NEW QUESTION # 188
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?

Answer: A


NEW QUESTION # 189
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?

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 # 190
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