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Title: AAIA Reliable Study Plan | Official AAIA Practice Test
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ISACA AAIA Exam Syllabus Topics:
TopicDetails
Topic 1
  • AI Operations: It covers managing AI-specific data needs¡ªincluding collection, quality, security, and classification¡ªapplying 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
  • 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
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q33-Q38):NEW QUESTION # 33
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?
Answer: D

NEW QUESTION # 34
Which of the following techniques BEST assesses an AI model's ability to generalize to new data?
Answer: B
Explanation:
K-fold cross validationpartitions the dataset intokequally sized folds, iteratively using one fold for testing and the remaining folds for training. By rotating through all folds, the model is evaluated on multiple, non- overlapping subsets of data, providing a robust estimate of generalization performance. This process reduces the risk of overfitting to a single train-test split and is explicitly recognized in AI testing techniques and data analytics practices relevant to AAIA.
Option A (failure mode effects analysis) is useful for understanding failure impacts but not primarily for generalization. Option B (unit testing) focuses on code components, not model generalization. Option D (metamorphic testing) is valuable for validating behavior under transformations, but the standard, widely accepted approach to assessing generalization remainscross validation, making option C the best answer.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Testing Techniques for AI Solutions (conventional and AI- specific testing).
ISACA AI and data analytics training content that discusses cross validation as a generalization assessment method.

NEW QUESTION # 35
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
Explanation:
An AI usage policy sets the foundation for safe, ethical, and effective AI deployment. According to the AAIA™ Study Guide, having an AI policy in place ensures that users understand acceptable behaviors, limitations, and responsibilities when interacting with AI tools.
"AI acceptable use policies promote governance by clearly outlining the dos and don'ts of AI interaction, preventing misuse and aligning user activity with organizational values and compliance standards." Other actions (B, C, D) are important in operations and risk management but should follow the establishment of governance protocols through a usage policy. Hence, A is the highest-priority prerequisite.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "olicy Frameworks for End-User AI Interaction"

NEW QUESTION # 36
An IS auditor is reviewing a dataset used by a university to train a predictive machine learning model. Which of the following MOST likely indicates risk that the model could not process all data and make necessary correlations?
Answer: C
Explanation:
A numeric field stored as an object (string) format (option C) indicates improper data typing. Models cannot correctly compute correlations or statistical relationships when numerical values are stored as textual data.
AAIA emphasizes that incorrect datatypes are one of the most common causes of ML model misbehavior, including:
* Failure to compute averages, correlations, or mathematical operations
* Silent errors in preprocessing
* Skewed learning patterns
* Incorrect feature importance evaluationsThus, the Final Grade Percent field in object format is the most significant indicator of processing risk.Options A, B, and D are valid datatypes for their respective fields and pose no inherent model processing risk.
References:
AAIA Domain 2: Data Quality, Data Types, and Preprocessing.
AAIA Domain 3: AI Readiness and Data Validation.

NEW QUESTION # 37
Which of the following would provide the BEST evidence to an IS auditor that an AI model's outputs are effectively controlled for bias?
Answer: D
Explanation:
To determine whether an AI model's outputs areeffectively controlled for bias, an auditor needsempirical performance evidenceacross demographic groups.Similar accuracy ranges across groups (A)demonstrate that the model performs equitably and does not disproportionately advantage or disadvantage specific populations. This aligns with AAIA's emphasis onfairness metrics, such as disparate error rates, equal opportunity, and demographic parity analyses.
Option B (fairness definition) is important for governance but does not prove fairness in practice. Option C may introduce historical human bias rather than mitigate it. Option D (transparency of development) supports explainability but does not validate fairness outcomes. Actualperformance parity across demographic groupsis the strongest evidence of bias control.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (fairness evaluation; bias assessments).
ISACA fairness guidance emphasizing cross-group performance comparison.

NEW QUESTION # 38
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