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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
  • 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 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q209-Q214):

NEW QUESTION # 209
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

Answer: C


NEW QUESTION # 210
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

Answer: A


NEW QUESTION # 211
When developing an audit plan, which of the following is MOST important specifically for the transparency of an AI application?

Answer: D

Explanation:
Explainability testing is essential for determining how and why an AI model produces specific outputs. Transparency is a key AAIA requirement, especially in regulated or high-impact environments.
Explainability testing ensures:
Stakeholders understand model decisions
Auditors can verify fairness and accuracy
Regulators can review decision logic
The organization can justify outcomes to affected individuals
Models align with ethical and governance expectations


NEW QUESTION # 212
Which of the following is the BEST way to ensure data fed into an AI model aligns with business objectives?

Answer: C

Explanation:
Documenting data input requirements (option C) ensures that all incoming data supports the business purpose, operational constraints, and intended use cases of the AI model.
AAIA highlights that aligning AI systems with business objectives starts withclear data specifications, including:
* Required fields and data formats
* Data quality thresholds
* Acceptable ranges and constraints
* Mandatory attributes
* Source system definitions
* Business rationale for each feature
Without documentation, data pipelines may ingest irrelevant, low-quality, or misaligned data, causing the model to drift away from business needs.
Normalization (A) improves preprocessing but does not ensure alignment.
Switching data sources (B) is premature without evaluating needs.
Defining new attributes (D) is secondary to documenting overall requirements.
References:
AAIA Domain 1: Business Alignment and Data Requirements
AAIA Domain 2: Input Specification Governance


NEW QUESTION # 213
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 # 214
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