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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit (AAIA) Exam
Exam Number:	AAIA
Exam Price:	USD 459 (member), USD 599 (non-member)
Certificate Validity Period:	Not publicly specified (requires ongoing CPE maintenance after certification)
Related Certifications:	CPA CIA CISA
Passing Score:	65%
Exam Format:	Closed-book, Computer-based, Multiple-choice, Remote proctoring or test center
Real Exam Qty:	55
Exam Duration:	120 minutes
Available Languages:	English
Recommended Training:	Official AAIA Training Course Providers ISACA AI Audit Training Resources
Exam Registration:	ISACA AAIA Certification Page
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based exam delivered via PSI test centers or remote proctoring
Pre Condition:	Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification
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 (Q31-Q36):

NEW QUESTION # 31
Which of the following BEST enables an AI model to solve complex problems involving the analysis of large volumes of unstructured data?

A. Deep learning
B. Natural language processing (NLP)
C. Support vector machines (SVM)
D. Reinforcement learning (RL)

Answer: A

Explanation:
Deep Learning, a subset of machine learning based on multi-layered neural networks, is specifically designed to handle high-dimensional, unstructured data such as images, audio, and complex text. Unlike traditional algorithms like SVM, deep learning models perform " automated feature extraction, " meaning they can identify relevant patterns without manual engineering. The AAIA™ manual highlights deep learning as the foundational technology for modern AI breakthroughs in computer vision and generative models. While NLP (Option C) is a field that uses deep learning, " Deep Learning " is the broader technical engine that enables the processing of massive unstructured datasets across all domains.

NEW QUESTION # 32
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:

A. significantly reduce data bias.
B. identify complex data patterns.
C. ensure full compliance with regulations.
D. eliminate the need for human intervention.

Answer: B

NEW QUESTION # 33
When utilizing a machine learning (ML) model to predict whether a wind turbine electricity generator will fail, which model evaluation metric should be the PRIMARY focus?

A. Specificity
B. Recall
C. Accuracy
D. Precision

Answer: B

Explanation:
In predictive maintenance use cases--such as detecting turbine failure--the most critical concern is identifying as many actual failures as possible to prevent catastrophic events. The AAIATM Study Guide emphasizes that in such high-risk scenarios, Recall is the most appropriate metric because it measures the proportion of true positives correctly identified.
"Recall is critical in scenarios where missing a positive instance (e.g., a failure) is costly or dangerous. It ensures that most real issues are caught by the model, even at the expense of some false positives." Precision measures correctness of positive predictions, specificity measures true negatives, and accuracy may be misleading if the data is imbalanced. Thus, D (Recall) is most appropriate.

NEW QUESTION # 34
Which of the following is the GREATEST risk when an organization uses publicly available social media data to train its AI model without obtaining explicit user consent?

A. Introduction of inaccurate and incomplete data
B. Violation of data privacy regulations leading to legal penalties
C. User distrust caused by lack of transparency in AI model training
D. Data with questionable labeling

Answer: B

Explanation:
Using social media data without consent raises severe legal and ethical concerns. According to the AAIATM manual, the greatest risk is the "Violation of data privacy regulations" (such as GDPR or CCPA), which can result in significant legal penalties, mandatory deletion of models, and litigation. Even if the data is "public," privacy laws often restrict the use of personal information for automated decision-making or profiling without a clear legal basis (like consent).

NEW QUESTION # 35
An IS auditor is planning an audit of an AI medical prognosis system. Which of the following is the BEST way to test model transparency?

A. Build clinical integration workflows
B. Document diagnostic tools for stress tests
C. Design output assessments for recognized healthcare standards
D. Use the SHapley Additive exPlanations (SHAP) method

Answer: D

Explanation:
SHAP (Shapley Additive Explanations) is the leading, industry-recognized method for explaining complex AI model predictions.
AAIA specifically identifies SHAP as a robust technique for transparency in high-impact systems such as medical AI.
SHAP provides:
* Feature contribution breakdowns
* Visual explanation of each decision
* Global and local interpretability
* Quantifiable insights into model logic
* Compliance-friendly justification of predictions
Options A and B relate to integration and stress testing, not transparency.
Option C validates outputs but does not explain how the model reached them.
Thus, SHAP is the best approach for achieving explainability in medical AI.
References:
AAIA Domain 5: Explainability Techniques
AAIA Domain 3: Model Validation and Auditability

NEW QUESTION # 36
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