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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 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 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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Free PDF 2026 ISACA AAIA: Newest Latest ISACA Advanced in AI Audit Braindumps Questions

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ISACA Advanced in AI Audit Sample Questions (Q61-Q66):

NEW QUESTION # 61
Which of the following should be an IS auditor's GREATEST concern if class imbalance is identified in training data for an AI model?

Answer: C

Explanation:
Class imbalanceoccurs when one or more classes are underrepresented in the training data. The GREATEST concern ismodel bias(C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate intosystematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).
Data drift (A) refers to changes in data distributions over time-related but distinct. Data quality (B) is broader and may or may not be affected by imbalance. Overfitting (D) is a risk, but class imbalance more directly raises fairness and representativeness concerns rather than overfitting alone. Thus,bias arising from class imbalanceis the auditor's primary concern.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Data Management Specific to AI (data balancing, bias risk).
ISACA AI ethics and model risk guidance discussing class imbalance and fairness.


NEW QUESTION # 62
An IS auditor uses an AI model to summarize worksheets, but several worksheets contained a
"Hidden Cell" instructing the model to ignore control failures. Which solution BEST mitigates the risk?

Answer: D

Explanation:
This is a "Data-based Prompt Injection," where the AI treats data as instructions. By "Allowing extraction only to predefined values and headers," the auditor restricts the AI's "Context Window" to specific, known data fields. This prevents the AI from "reading" and executing malicious commands hidden in other cells.


NEW QUESTION # 63
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?

Answer: B

Explanation:
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIATM Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.


NEW QUESTION # 64
Which control is MOST important to verify in order to ensure proper data management with AI systems?

Answer: D

Explanation:
According to the ISACA AAIATM Study Guide, a "Data Governance Framework" is the foundational control. It provides the policies, roles (stewards, owners), and standards necessary to manage the entire data lifecycle. Without a framework, activities like inventorying (Option C) or labeling (Option D) are ad-hoc and lack accountability. A formal framework ensures that data management is consistent, compliant with privacy laws, and aligned with the organization's risk tolerance. It is the "enabling" control that makes all other data quality and security metrics meaningful and enforceable.


NEW QUESTION # 65
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

Answer: B


NEW QUESTION # 66
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