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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q49-Q54):

NEW QUESTION # 49
Which of the following should be applied to an AI system but are not typically used in traditional systems?

A. Controls to monitor data poisoning
B. Controls to manage data governance
C. Controls to protect data privacy
D. Controls to prevent data exfiltration

Answer: A

Explanation:
AI systems face unique threats not commonly found in traditional IT environments, particularly data poisoning, where attackers manipulate training data to corrupt model behavior. Controls that specifically monitor and mitigate poisoning--such as input provenance checks, anomaly detection on training data, and integrity validation pipelines--are emphasized in AAIA's coverage of AI- specific vulnerabilities.

NEW QUESTION # 50
From an audit perspective, which of the following BEST supports the objectives of an AI governance program?

A. Defining roles, responsibilities, and training for all stakeholders on AI ethics
B. Defining AI policies for model interpretability, transparency, and resiliency
C. Developing an AI risk register to record potential algorithmic bias
D. Establishing an AI ethics committee or responsible AI review board

Answer: B

Explanation:
While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the " AI Policies " themselves are the most comprehensive support for governance objectives.
Policies provide the " enforceable standards " for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization's AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the " Source of Truth " that guides committees and stakeholders in their decision-making processes.

NEW QUESTION # 51
An organization deploys an AI recruitment platform to screen job applicants. The IS auditor identifies that the platform's decisions may be influenced by model bias. Which of the following risk mitigation strategies is BEST for the auditor to recommend?

A. Implement a process to periodically test the AI system for biases and adjust parameters as needed.
B. Require manual reviews of all AI-generated recruitment decisions before hiring is finalized.
C. Suspend the use of the AI system until the training data can be verified for fairness and compliance.
D. Retrain the AI model using an external data set certified for inclusivity and fairness.

Answer: A

NEW QUESTION # 52
Which of the following BEST demonstrates "explainability by design"?

A. Hiring a customer support team
B. Selecting inherently interpretable models or building explainability tooling into the model development lifecycle from the start
C. Adding a FAQ page for end users after deployment
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 # 53
In order to streamline operations, a bank has deployed an AI application to automatically detect and prevent further fraud on accounts. However, customers have voiced concerns that their usual transactions are being rejected. Which of the following is the MOST likely cause of the false positives?

A. Compute scale training was not performed.
B. Consent is not properly managed.
C. The hyperparameters are not optimized.
D. Data versioning controls were not developed.

Answer: C

Explanation:
False positives in fraud detection AI systems often stem from poorly optimized hyperparameters.
Hyperparameters control aspects of the model's learning process such as the learning rate, decision thresholds, and complexity penalties. When these parameters are not tuned correctly, the model can become overly sensitive and flag normal behavior as suspicious, leading to customer complaints.
"Hyperparameter tuning is essential to balance sensitivity and specificity in AI models. Improper tuning can result in a high rate of false positives or negatives, particularly in systems like fraud detection that require nuanced pattern recognition." Options A and B relate to data governance but do not directly cause false positives in predictions. Option C (compute scale training) may affect model efficiency, not accuracy. Thus, D is the most appropriate answer.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Model Tuning and Optimization"

NEW QUESTION # 54
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