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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 (Q194-Q199):

NEW QUESTION # 194
Which of the following is the MOST effective control to prevent data poisoning attacks during model training?

Answer: A

Explanation:
Data poisoning involves injecting malicious or mislabeled data into the training set. Provenance verification and data validation pipelines reduce the likelihood of compromised data reaching the model.


NEW QUESTION # 195
From a data appropriateness and bias perspective, which of the following should be of GREATEST concern when reviewing an AI model used in a credit scoring system?

Answer: B

Explanation:
Using postal codes as a primary factor in credit scoring raises concerns of geographic and socioeconomic bias. Postal codes can serve as proxies for race, ethnicity, or income level-- potentially violating fair lending laws and ethical guidelines.
"Auditors should flag models that rely on proxies for protected attributes. Postal codes are high- risk features that may inadvertently lead to redlining or other discriminatory practices."


NEW QUESTION # 196
Which of the following is the GREATEST risk when training data is not separated into distinct training and testing sets?

Answer: B

Explanation:
If training and testing sets are not separated, the model evaluates itself on the same data it was trained on, creating a false sense of accuracy. The result isoverfitting(option A): the model learns the training data too well and fails to generalize to new data.
AAIA emphasizes that proper data splitting is foundational to machine learning evaluation. Overfitting undermines real-world performance, creates untrustworthy predictions, and hides bias or errors.
Model drift (B) occurs after deployment. Hallucinations (C) relate more to generative models. Underfitting (D) occurs when the model is too simple, not from lack of dataset separation.
Thus, overfitting is the direct and greatest risk when training and testing sets are not segregated.
References:
AAIA Domain 2: Testing Techniques and Model Evaluation Standards.
AAIA Domain 1: Fundamentals of ML model training.


NEW QUESTION # 197
Which of the following is the MOST important task when gathering data during the AI system development process?

Answer: C

Explanation:
Data cleaning is a foundational task in the AI development lifecycle. The AAIA™ Study Guide identifies data quality-ensuring completeness, accuracy, consistency, and correctness-as critical to building effective and unbiased AI systems. Cleaning the data involves removing duplicates, correcting errors, addressing missing values, and standardizing formats.
"Data cleaning is a prerequisite for effective training and evaluation. Poor-quality data leads to inaccurate or misleading model outputs, increasing operational and ethical risks." While training (D) is essential, it must occur only after the data has been adequately prepared. Stratification (A) supports certain modeling approaches but is secondary to data integrity. Therefore, C is the most important task at the data-gathering stage.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Collection and Preparation"


NEW QUESTION # 198
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 # 199
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