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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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.

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ISACA Advanced in AI Audit Sample Questions (Q44-Q49):

NEW QUESTION # 44
A financial institution's customer service chatbot sometimes gives users incorrect legal advice, leading to several customer complaints and potential regulatory exposure. Which of the following should be done FIRST to mitigate this risk?

Answer: B

Explanation:
When a customer-facing system is providing harmful or incorrect advice (particularly legal or financial), the "Immediate Priority" is to prevent further harm. "Introducing a human-in-the-loop review process" ensures that a qualified human checks and approves the chatbot's advice before it is sent to the customer. This acts as an immediate safety "guardrail."


NEW QUESTION # 45
During an audit of an investment organization's AI-powered software, an IS auditor identifies a potential security risk. What is the GREATEST risk associated with staff exfiltrating organizational data to a generative AI tool?

Answer: D


NEW QUESTION # 46
An IS auditor notes that the combined number of records in the training, validation, and testing sets exceeds the total number of records in the original data set. What is the GREATEST risk?

Answer: B

Explanation:
In a proper AI pipeline, the original data must be "strictly partitioned" into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were "Reused" across sets.
This leads to "Data Leakage," where the model "sees" the test or validation data during the training phase. As a result, the model's performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIATM Study Guide identifies data leakage as a primary cause of "Model Evaluation Failure," rendering all testing results invalid.


NEW QUESTION # 47
During an AI audit, what is the PRIMARY reason to review data lineage documentation?

Answer: B

Explanation:
Data lineage provides traceability of data's origin and transformations, which is essential for verifying data quality, consent compliance, and reproducibility of model behavior.


NEW QUESTION # 48
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?

Answer: C

Explanation:
Ethical risk begins with the nature of the data being used. The sensitivity and origin of training data (option B) determine whether the model risks violating privacy, perpetuating historical bias, or using data that was collected without proper consent.
AAIA identifies data provenance, data sensitivity, and lawfulness of processing as central to ethical AI. Training data that contains sensitive attributes (e.g., health, ethnicity, gender, financial status) must be reviewed carefully for compliance and ethical impacts.


NEW QUESTION # 49
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