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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Passing Score:	450 (scaled, range 200–800)
Certificate Validity Period:	3 years
Related Certifications:	CISA ACCA FCCA CIA CPA
Exam Price:	US$459 (member) / US$599 (non-member)
Exam Duration:	150 minutes
Exam Format:	Scenario-based questions, Multiple-choice questions, Computer-based testing
Real Exam Qty:	90
Available Languages:	English, Chinese, Spanish
Recommended Training:	AAIA Review Manual AAIA Official Review Course
Exam Registration:	ISACA Official Registration
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong)
Pre Condition:	Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline

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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 (Q291-Q296):

NEW QUESTION # 291
A bank's fraud detection model achieves high accuracy on its initial dataset but performs poorly in production. The data science team needs to tune hyperparameters and select the best model architecture. Which dataset is BEST to use for this selection process?

A. Testing dataset
B. Training dataset
C. Validation dataset
D. Holdout dataset

Answer: C

Explanation:
In the standard machine learning workflow, the "Validation Dataset" is specifically used for "model selection" and "hyperparameter tuning." It serves as a bridge between training and the final test.
Using the training set (Option B) for tuning would lead to overfitting. The "Testing" or "Holdout" set (Options A and C) must remain completely "unseen" until the very end to provide an unbiased final estimate of how the model will perform in the real world. According to the ISACA AAIATM manual, maintaining this strict partitioning is critical for model integrity and preventing overly optimistic performance reports.

NEW QUESTION # 292
When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?

A. Predictive analytics
B. Autoregressive sequencing model
C. Natural language processing
D. Robotic process automation (RPA)

Answer: C

NEW QUESTION # 293
An auditor is reviewing consent management for an AI system trained on customer data. Which finding would be the MOST significant concern?

A. Consent was collected via a clear opt-in form
B. Consent records are stored in a secure database
C. Customer data collected for one purpose (e.g., service delivery) was repurposed for AI model training without additional consent
D. Consent forms are reviewed annually

Answer: C

Explanation:
Using data beyond its originally consented purpose (purpose limitation violation) is a significant privacy and regulatory risk, particularly under frameworks like GDPR.

NEW QUESTION # 294
An IS auditor is reviewing a dataset used by a university to train a predictive machine learning model. Which of the following MOST likely indicates risk that the model could not process all data and make necessary correlations?

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

Answer: C

Explanation:
A numeric field stored as an object (string) format (option C) indicates improper data typing.
Models cannot correctly compute correlations or statistical relationships when numerical values are stored as textual data.
AAIA emphasizes that incorrect datatypes are one of the most common causes of ML model misbehavior, including:
Failure to compute averages, correlations, or mathematical operations
Silent errors in preprocessing
Skewed learning patterns
Incorrect feature importance evaluations
Thus, the Final Grade Percent field in object format is the most significant indicator of processing risk.

NEW QUESTION # 295
An insurance company uses an AI model to set premium rates. To align with AI-related policies on fairness, which of the following is the FIRST course of action?

A. Reviewing AI model training data to identify potential biases
B. Allowing customers to contest premium rates provided by the AI model
C. Modifying the AI model's training dataset to address potential biases
D. Training alternate AI models and comparing biases with the primary model

Answer: A

Explanation:
AAIA guidance states that fairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, the first course of action is to review training data (option B) to identify:
Skewed demographic distributions
Missing or underrepresented populations
Inappropriate use of sensitive attributes
Incorrect labels or improperly encoded variables
Historical decisions that may propagate discrimination

NEW QUESTION # 296
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