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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 (Q69-Q74):

NEW QUESTION # 69
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?

A. Determine whether the information is reliable.
B. Validate that the tool is leveraging the latest model.
C. Determine whether any AI model hallucinations have occurred.
D. Validate that the tool provides a privacy notice.

Answer: A

NEW QUESTION # 70
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. Precision
B. Recall
C. Accuracy
D. Specificity

Answer: B

NEW QUESTION # 71
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

A. Deploying two separate copies of the model after each adjustment to compare results
B. Documenting model updates and retraining sessions to ensure traceability
C. Engaging an independent expert to review the model's accuracy and precision on a quarterly basis
D. Assigning a single data science team member to adjust the model in order to establish accountability

Answer: B

NEW QUESTION # 72
Which use case for an AI model to be used by a food delivery service would pose ethical risk to the organization?

A. Using customer service metrics for service speed and food quality to predict customer retention and forecast revenue
B. Comparing total food preparation and delivery time to an industry benchmark to set key performance and risk indicators for individual restaurants
C. Correlating time, cost, delivery distance, and customer satisfaction metrics to issue coupons to customers receiving substandard service
D. Basing driver retention and termination decisions on the number of delivered orders per total hours worked as compared to an industry benchmark

Answer: D

NEW QUESTION # 73
Which of the following metrics are the BEST indication of a mature and effective approach to an organization's data governance program for its AI systems?

A. Percentage of AI models with documented data lineage
B. Number of AI projects completed within the last fiscal year
C. Total budget allocated to AI initiatives across all departments
D. Frequency of data quality audits on the organization's data sets

Answer: A

NEW QUESTION # 74
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Emma Bailey Emma Bailey

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Paras Chess Academy

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