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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Real Exam Qty:	90
Exam Format:	Multiple Choice, Computer-Based, Remotely Proctored
Exam Duration:	120 minutes
Related Certifications:	CIA CPA FCCA ACCA CISA
Passing Score:	65%
Available Languages:	English
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 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.

ISACA Advanced in AI Audit Sample Questions (Q38-Q43):

NEW QUESTION # 38
An IS auditor is reviewing a dataset used by a university. Which of the following MOST likely indicates a risk that the model could not process all data and make necessary correlations?

A. Grade Level field in float format
B. Having an undergraduate degree in Boolean format
C. Final Grade Percent field in object format
D. Student Number field in integer format

Answer: C

Explanation:
In machine learning, "Data Typing" is foundational. If a numerical field (like Grade Percent) is stored as an "Object" (string/text), the AI model cannot perform mathematical operations on it, such as calculating correlations or averages. According to the AAIATM manual, this is a "Data Quality" failure that leads to "Feature Exclusion," where the model simply ignores the variable or treats each number as a unique text label, losing all quantitative meaning. The other formats (Integer, Float, Boolean) are appropriate for their respective data types.

NEW QUESTION # 39
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?

A. Provide periodic model audit reports.
B. Create dashboards to show outputs.
C. Document sources and data processes.
D. Use tools that explain model decisions.

Answer: D

NEW QUESTION # 40
Which of the following is the MOST significant benefit of performing frequent AI model testing and retraining?

A. Ensuring model scalability
B. Ensuring the model is updated with current data
C. Removing unnecessary model features
D. Assessing the impact of attacks

Answer: B

Explanation:
AI models are not " static " systems; they are reflections of the data they were trained on. In dynamic environments, data patterns change constantly (e.g., market trends or new fraud tactics). Frequent testing and retraining ensure the model is " updated with current data, " which prevents performance degradation and " data drift. " While testing can assess attacks (Option A), the primary operational reason for a recurring retraining schedule is to maintain the model ' s relevance and accuracy against the most recent real-world behaviors.

NEW QUESTION # 41
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?

A. Transfer learning using public health data sets
B. Data augmentation during training to improve privacy
C. Supervised learning with labeled patient records
D. Differential privacy applied during model training

Answer: D

Explanation:
Differential privacyintroduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data-highly sensitive and subject to strict privacy laws-this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.
Option A uses real patient records directly and does not, by itself, mitigate inference risk. Option B (data augmentation) may expand the dataset but does not guarantee resistance to membership inference attacks.
Option D (transfer learning using public data) can help, but if any private data is used in fine-tuning, privacy risks remain. Differential privacy, as in option C, is the most appropriate control to ensure that outputs do not reveal whether particular personal data was used.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs; Domain 2: Data Management Specific to AI (data confidentiality, data security).
ISACA guidance on privacy-by-design and AI risk management concepts reflected in AAIA.

NEW QUESTION # 42
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

A. Placing numerical data into bins or buckets for a manageable quantity of correlations and result analyses
B. Identifying and deleting duplicate entries in the data set
C. Separating multiple data attributes within one field into individual attribute columns
D. Filling blank attributes in records with the mean, median, or mode within a grouping

Answer: D

NEW QUESTION # 43
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