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ISACA AAIA Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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 (Q194-Q199):

NEW QUESTION # 194
Which of the following is the BEST recommendation to mitigate excessive agency when implementing an AI system as a browser extension?

A. Maximize browser extension functionality.
B. Remove user access to browser extensions.
C. Minimize browser extension functionality.
D. Use open-source browser extensions.

Answer: C

Explanation:
Excessive agencyoccurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is tominimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance onlimiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.
Removing user access entirely (B) is overly restrictive. Maximizing functionality (C) increases risk. Open- source extensions (D) improve transparency but do not inherently control excessive agency.Scope limitation is the most targeted and effective mitigation.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Governance, AI Autonomy, and Risk Controls.

NEW QUESTION # 195
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

A. Placing numerical data into bins or buckets for a manageable quantity of correlations and result analyses
B. Separating multiple data attributes within one field into individual attribute columns
C. Identifying and deleting duplicate entries in the data set
D. Filling blank attributes in records with the mean, median, or mode within a grouping

Answer: D

NEW QUESTION # 196
When auditing AI systems, which data management practice BEST ensures compliance with privacy regulations?

A. Using multiple data sources across different regions
B. Retaining raw data indefinitely for future use
C. Implementing data minimization principles during data collection
D. Increasing dataset size to improve model accuracy

Answer: C

Explanation:
Privacy regulations like GDPR and CCPA emphasize the principle of " Data Minimization " -collecting only the data that is strictly necessary for the intended purpose. In an AI context, organizations often feel tempted to collect as much data as possible to improve accuracy (Option A), but this increases privacy risk and legal exposure. According to ISACA, implementing data minimization is the most effective practice for ensuring regulatory compliance. It reduces the " attack surface " in the event of a breach and ensures that the organization is not holding onto sensitive PII that it cannot justify. Indefinite retention (Option D) is a direct violation of most modern privacy laws.

NEW QUESTION # 197
A newly deployed fraud detection model is misclassifying transactions due to inconsistent formatting in the data stream. What is the BEST recommendation?

A. Train staff on fraud patterns and alert handling
B. Increase model complexity to handle more input types
C. Enable real-time monitoring of transaction volumes
D. Define and document the technical specifications for incoming data

Answer: D

Explanation:
Inconsistent data formatting means the AI model is receiving inputs that do not match what it was trained to understand. The best corrective action is todocument and enforce technical specificationsfor incoming data (option A).
AAIA highlights this as a fundamental data governance requirement:
* Field formats
* Data types
* Encoding rules
* Required attributes
* Normalization methods
Without strict specifications, the model cannot reliably parse or classify inputs.
Option B does not address the root cause.
Option C improves human response but not data consistency.
Increasing model complexity (D) adds risk and does not fix inconsistent formatting.
References:
AAIA Domain 2: Input Standards and Data Consistency
AAIA Domain 1: Governance and Data Pipeline Controls

NEW QUESTION # 198
An IS auditor is testing an AI model used for determining insurance premiums and eligibility. Which of the following is the MOST effective testing method to identify bias in algorithm outputs?

A. Disparate impact analysis
B. Predictive analytics
C. Regression testing
D. Cross-cluster analysis

Answer: A

Explanation:
Disparate impact analysisis a specific technique used to detect whether model decisions disproportionately disadvantage certain protected or sensitive groups (e.g., by gender, age, ethnicity, or other attributes). For an AI model determiningpremiums and eligibility, fairness and non-discrimination are critical regulatory and ethical requirements, and AAIA content highlights fairness and bias evaluation as core elements of AI governance and risk management.
Regression testing (A) checks that changes do not introduce defects in previously functioning components, not fairness. Cross-cluster analysis (B) may reveal patterns but is not inherently a fairness test. Predictive analytics (D) is a broad term for forward-looking analysis, not a method specifically designed to detect bias.
Therefore,disparate impact analysisis the most appropriate and targeted method to identify bias in the insurance AI model's outputs.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (bias, fairness, and transparency considerations).
ISACA AI ethics and fairness guidance used in AAIA training materials.

NEW QUESTION # 199
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