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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 (Q201-Q206):

NEW QUESTION # 201
Which of the following techniques BEST enhances the security and robustness of AI models against adversarial attacks?

Answer: D

Explanation:
Defensive distillation is a specialized technique designed to make an AI model more robust against adversarial "perturbations"--small changes to input data intended to trick the model. It works by training a smaller model to mimic the probability distributions of a larger, pre-trained model, which effectively "smooths" the decision boundaries and makes it harder for attackers to find exploitable gaps. While access reviews and awareness training are standard security controls, they do not address the mathematical vulnerabilities inherent in ML algorithms.
Defensive distillation specifically targets the model's resilience to technical exploitation.


NEW QUESTION # 202
An IS auditor is reviewing change documentation of an AI model. Which of the following would pose the GREATEST risk?

Answer: A

Explanation:
Alow number of test scenarios(C) indicates insufficient testing coverage, creating a high likelihood that errors, biases, or edge-case failures remain undetected. AI systems requirebroad, diverse scenario testing, including fairness, robustness, and stress testing. AAIA highlights comprehensive testing as essential to AI reliability and risk mitigation.
Lack of standardized test formats (B) is inefficient but less severe. Missing seed documentation (D) affects reproducibility but not completeness. Lack of management testing (A) matters, but insufficient test scenarios pose thelargest direct riskto model safety and performance.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Testing Techniques for AI Solutions.


NEW QUESTION # 203
An IS auditor reviewed an AI-enabled software for processing a bank's financial information and discovered errors in the training data. Which of the following would BEST mitigate this risk?

Answer: C

Explanation:
When errors are discovered in training data, the primary risk is that themodel has learned from incorrect or low-quality inputs, which can systematically distort outputs (e.g., misstatements of financial information, incorrect classifications, or erroneous flags).Data quality testingdirectly targets this issue by assessing completeness, accuracy, consistency, validity, and integrity of the data used to train, validate, and test AI models.
Functional testing (A) focuses on whether the application behaves according to requirements, not specifically on the underlying data quality. User interface testing (C) concerns usability and presentation, not training data. Model validation on benchmark data (D) helps assess performance but may not reveal or correct root# cause issues in the underlying training data if those benchmarks are also flawed or unrepresentative. AAIA content emphasizes thatdata quality controls are foundationalto AI reliability and must be treated as a core operational control.
References:
ISACA,Advanced in AI Audit (AAIA) Exam Content Outline- Domain 2: Data Management Specific to AI (data quality, data balancing, data security).
ISACA AI guidance on data quality controls as the basis of trustworthy AI outcomes.


NEW QUESTION # 204
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?

Answer: D

Explanation:
Ethical risk begins with the nature of the data being used. The sensitivity and origin of training data (option B) determine whether the model risks violating privacy, perpetuating historical bias, or using data that was collected without proper consent.
AAIA identifies data provenance, data sensitivity, and lawfulness of processing as central to ethical AI.
Training data that contains sensitive attributes (e.g., health, ethnicity, gender, financial status) must be reviewed carefully for compliance and ethical impacts.
Option A (diverse outputs) relates to performance, not ethics. Option C (update frequency) is part of lifecycle management, not ethical sourcing. Option D (cleaning methods) ensures quality but does not address ethical risk if the underlying data is inappropriate or unlawfully sourced.
Thus, sensitivity and origin of training data are the primary ethical factors.
References:
AAIA Domain 5: Ethical Principles, Fairness, Data Sensitivity Considerations.
AAIA Domain 1: Privacy and Data Governance Programs.


NEW QUESTION # 205
An organization developed an AI model trained on its monthly data. Which of the following would be the BEST validation method to avoid data drift?

Answer: B

Explanation:
When dealing with monthly or temporal data, standard random splits (Option D) or cross- validation (Option A) can cause "temporal leakage," where the model inadvertently learns from future data to predict the past. According to ISACA AAIATM principles, "Time Series" validation is the most appropriate method for sequential data. It involves training the model on a specific period (e.g., months 1?0) and testing it on the subsequent period (e.g., month 11). This approach accurately reflects how the model will perform in production and is essential for detecting data drift, as it identifies when seasonal trends or long-term shifts in customer behavior cause the model's accuracy to degrade over time.


NEW QUESTION # 206
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