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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.

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ISACA Advanced in AI Audit Sample Questions (Q180-Q185):

NEW QUESTION # 180
Which of the following is the MOST effective way an IS auditor could use generative AI to plan an audit of a new database storing transactional data?

A. Identifying separation of duties conflicts for database data changes
B. Developing architecture diagrams
C. Identifying technology-specific risk and considerations
D. Summarizing meeting transcripts from interviews with database administrators (DBAs)

Answer: C

Explanation:
Generative AI excels at synthesizing large datasets and technical documentation into understandable insights. The AAIATM Study Guide recommends leveraging generative AI to identify domain-specific risks and control considerations by analyzing complex environments and correlating them with industry risk patterns.
"AI can assist auditors during planning by generating tailored risk profiles for technologies under review, helping prioritize audit focus and scoping."

NEW QUESTION # 181
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?

A. Model inversion
B. Model drift
C. Model poisoning
D. Model extraction

Answer: B

Explanation:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIATM manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations.

NEW QUESTION # 182
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?

A. Placing numerical data into bins or buckets for a manageable quantity of correlations and result analyses
B. Identifying and deleting duplicate entries in the data set
C. Separating multiple data attributes within one field into individual attribute columns
D. Filling blank attributes in records with the mean, median, or mode within a grouping

Answer: D

NEW QUESTION # 183
What is the MOST appropriate control to address the risk of an AI chatbot providing harmful medical advice to users?

A. A larger training dataset
B. Faster response times
C. Increasing server capacity
D. Content guardrails, scope restrictions, and mandatory disclaimers directing users to licensed professionals for medical decisions

Answer: D

Explanation:
Guardrails that restrict the chatbot's scope, combined with disclaimers and escalation paths to qualified professionals, mitigate the risk of harm from unqualified AI-generated medical guidance.

NEW QUESTION # 184
During a risk assessment for an AI system, data drift was identified as a key risk. Which of the following is the BEST course of action?

A. Retrain the model immediately using the same data set.
B. Archive the training data and proceed with deployment.
C. Document the risk and implement continuous monitoring.
D. Disable the AI system until risk is eliminated.

Answer: C

Explanation:
Data drift occurs when the statistical properties of input data change over time, causing the AI model to produce increasingly inaccurate or biased results.
The correct response is todocument the risk and implement continuous monitoring(A) because:
* Drift requiresongoing detection, not a one-time fix
* Retraining too early can reinforce issues (especially with the same dataset)
* Disabling the system (D) is overly disruptive unless drift results in unsafe decisions
* Continuing deployment without monitoring (C) increases risk of degraded performance AAIA emphasizesdrift monitoring dashboards, alerting mechanisms, and retraining triggersas essential controls for AI operations.
References:
AAIA Domain 2: AI Operations - Monitoring, Drift Detection, and Lifecycle Maintenance

NEW QUESTION # 185
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