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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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ISACA Advanced in AI Audit Sample Questions (Q249-Q254):

NEW QUESTION # 249
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?

Answer: B

Explanation:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIA™ manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations. Options A, B, and C refer to security attacks, whereas " Model drift " is the natural operational degradation that occurs when AI systems are not regularly updated with fresh, representative data.


NEW QUESTION # 250
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

Answer: C

Explanation:
An AI model inventory documents the models in use, their purposes, and how they support specific business functions. According to the AAIA™ Study Guide, maintaining a comprehensive AI model inventory allows auditors to trace model objectives, performance metrics, and use cases back to business goals.
"A well-maintained AI model inventory supports governance and alignment by offering a centralized view of model functions, business integration, and ownership. It ensures transparency and strategic coherence." While policies and assessments are important, only the inventory directly shows which AI models exist and their connection to organizational objectives.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Evidence and Documentation of AI Strategy Alignment"


NEW QUESTION # 251
Which of the following techniques BEST assesses an AI model's ability to generalize to new data?

Answer: D

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 # 252
Which of the following would be the BEST time to create a data governance plan for a new AI solution?

Answer: C

Explanation:
In AI development, " Privacy by Design " and " Governance by Design " are fundamental principles. The data governance plan should be created " As part of the initial design " phase. This ensures that the requirements for data quality, lineage, labeling, and privacy are integrated into the architecture from the beginning.
According to the AAIA™ framework, attempting to retrofit governance during the testing (Option A) or production (Option B) phases is often impossible or cost-prohibitive, as the data may have already been corrupted or collected in a non-compliant manner. Planning at the design stage mitigates the risk of building on a flawed foundation.


NEW QUESTION # 253
Which use case for an AI model to be used by a food delivery service would pose ethical risk to the organization?

Answer: B

Explanation:
Using AI to make employment decisions such as driver termination or retention introduces significant ethical risks. If based solely on performance metrics without context or human review, such systems can lead to unfair treatment or discrimination--violating principles of transparency and due process.
"Automating workforce decisions must be approached cautiously to prevent discriminatory outcomes. Ethical AI governance requires oversight when AI is used for employment-impacting decisions."


NEW QUESTION # 254
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