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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Exam Format:	Multiple Choice, Computer-Based, Remotely Proctored
Real Exam Qty:	90
Related Certifications:	CPA FCCA ACCA CISA CIA
Available Languages:	English
Exam Duration:	120 minutes
Passing Score:	65%
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Online remotely proctored computer-based exam
Pre Condition:	Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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 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	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.

ISACA Advanced in AI Audit Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which of the following BEST demonstrates "explainability by design"?

A. Adding a FAQ page for end users after deployment
B. Selecting inherently interpretable models or building explainability tooling into the model development lifecycle from the start
C. Hiring a customer support team
D. Publishing the model's accuracy score publicly

Answer: B

Explanation:
Explainability by design integrates interpretability considerations (model choice, feature transparency, explanation tooling) into development from the outset, rather than retrofitting explanations after deployment.

NEW QUESTION # 51
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training dat a. Which of the following is the auditor's BEST recommendation to address this issue?

A. Suspend the use of the platform until the training data is sanitized.
B. Use a platform that certifies the provenance and licensing of its training data.
C. Label all AI-generated images to disclaim the possibility of third-party content.
D. Implement a manual review process to ensure no copyrighted images are used in generated outputs.

Answer: B

NEW QUESTION # 52
A healthcare AI tool recommends treatments with high success rates but significant risk. The hospital prioritizes patient safety over innovation. What is the BEST course of action?

A. Adjust the AI's parameters to align with the hospital's risk tolerance.
B. Obtain patients' consent for the use of their data by the AI tool.
C. Use the AI tool only for low-risk situations.
D. Discontinue using the AI tool and rely solely on doctor expertise.

Answer: A

Explanation:
AI systems must align with the organization's risk appetite and ethical principles, especially in healthcare where patient safety is paramount. The BEST action is to adjust the AI's parameters (A) so the recommendations reflect the hospital's conservative risk tolerance, reducing the frequency of high-risk suggestions. AAIA stresses AI governance alignment with organizational risk appetite, treatment guidelines, and ethical priorities.

NEW QUESTION # 53
Which of the following types of AI can use unlabeled data sets to imitate human learning behavior?

A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Federated learning

Answer: B

Explanation:
Unsupervised learningusesunlabeled datato discover patterns, structures, or groupings without explicit outcome labels. The model "learns" by identifying similarities, clusters, or latent structures within the data, somewhat analogous to how humans can notice patterns without being told the correct answer. In AAIA's fundamentals coverage, unsupervised methods (e.g., clustering, dimensionality reduction) are explicitly linked to situations wherelabels are unavailable or costly, yet insight is still needed.
Supervised learning (A) requires labeled examples (input-output pairs). Federated learning (B) describes a distributed training paradigm, not the labeling requirement itself. Reinforcement learning (C) uses feedback in the form of rewards and penalties rather than unlabeled static datasets. Therefore,unsupervised learningis the correct type that directly usesunlabeled datato learn structure.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Models, Considerations, and Requirements (supervised, unsupervised, reinforcement learning).
ISACA AI fundamentals content on learning types and data labeling.

NEW QUESTION # 54
Which of the following techniques is BEST to use when there is a limited dataset of detailed images available to train a convolutional neural network (CNN) model?

A. Duplication of current dataset
B. Transfer learning from an open-source model
C. Splitting the dataset 50/50 between training and testing sets
D. Minority class undersampling

Answer: B

Explanation:
When data is scarce, training a deep CNN from scratch is likely to result in overfitting. "Transfer learning" allows an organization to take a model previously trained on a massive dataset (like ImageNet) and "fine-tune" it for their specific, smaller dataset. The model already knows how to recognize basic shapes and textures, so it requires much less data to learn specific new categories. This is a common and highly effective practice in medical imaging or specialized manufacturing audits.

NEW QUESTION # 55
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