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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

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ISACA Advanced in AI Audit Sample Questions (Q136-Q141):

NEW QUESTION # 136
An organization utilizes an off-the-shelf generative AI solution for internal business processing. According to the principle of shared responsibility, which of the following areas remains the PRIMARY responsibility of the organization?

A. Defining appropriate usage policies and enforcing identity and access management (IAM)
B. Maintaining the underlying AI compute infrastructure
C. Conducting initial training of the base foundation model
D. Ensuring the model ' s core safety and security systems are robustly designed

Answer: A

Explanation:
In a " Software as a Service " (SaaS) or off-the-shelf AI model deployment, the " Shared Responsibility Model " dictates that the vendor is responsible for the " Security OF the model " (infrastructure, base training, core safety), while the customer is responsible for the " Security IN the model " (usage). The organization ' s primary duty is " Defining usage policies and enforcing IAM " to ensure that only authorized employees use the tool and that they do not input sensitive corporate data into unvetted prompts. This governance ensures the AI is used ethically and securely within the organization's specific operational context.

NEW QUESTION # 137
An IS auditor is auditing an AI system that predicts inventory needs. The system recently failed to predict a stock outage for a key product. Which of the following audit tests would BEST validate the system's accuracy?

A. Load testing during peak sales periods
B. Unit testing of the forecasting algorithm
C. Sensitivity analysis on input variables
D. Historical testing with past sales data

Answer: D

Explanation:
The best way to validate the accuracy of a predictive AI system is to use historical testing with past sales data (option D). According to the AAIA™ Study Guide, "historical (or back-testing) is essential for evaluating how well a model would have performed using actual data from previous periods, directly reflecting its predictive validity." This method reveals any gaps or biases in the model by comparing predictions to known outcomes.
Unit testing, load testing, and sensitivity analysis are useful for technical verification and robustness but do not provide direct evidence of prediction accuracy in real-world scenarios.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Validation Techniques"

NEW QUESTION # 138
Which of the following key performance indicators (KPIs) are MOST important when evaluating whether an AI model meets business objectives?

A. Number of users interacting with the AI model
B. Cost of resources required for AI model training
C. Frequency of AI model retraining
D. AI model accuracy in predicting actual outcomes

Answer: D

Explanation:
The primary goal of any AI system is to provide predictions or classifications that support business decisions.
The AAIA™ Study Guide highlights that model accuracy-especially when validated against actual outcomes-is the most reliable indicator of whether the AI supports organizational goals effectively.
"Accuracy, precision, and recall are foundational metrics that indicate whether a model is performing in line with its intended objectives. High user engagement or retraining frequency does not confirm effective decision support unless the outputs are correct." Cost and user numbers offer useful operational insights but do not reflect the alignment of AI performance with strategic goals. Thus, D is the most meaningful KPI in this context.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "AI Metrics and Business Alignment"

NEW QUESTION # 139
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

A. AI model vulnerability assessment
B. AI change management requests
C. AI model inventory
D. AI acceptable use policy

Answer: C

NEW QUESTION # 140
Which of the following is the GREATEST risk when a generative AI tool used for threat detection produces inaccurate or misleading information?

A. Prompt injection attacks may become more likely to succeed.
B. AI-related key risk indicator (KRI) values may become less reliable.
C. Potential threats to organizational systems may be overlooked.
D. The model may exaggerate the severity of threats and vulnerabilities.

Answer: C

Explanation:
In the context of cybersecurity and threat detection, the "accuracy" of AI outputs is a matter of organizational safety. The greatest risk of inaccurate information (such as false negatives) is that
"Potential threats may be overlooked," leading to undetected breaches or system compromises.
This occurs when the model fails to flag a genuine anomaly as suspicious.

NEW QUESTION # 141
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