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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 (Q150-Q155):

NEW QUESTION # 150
An IS auditor for a veterinary clinic was informed that the dog breed categorical variable is necessary for the predictive model. Which of the following introduces the MOST risk?

A. Data scaling was not utilized.
B. One-hot encoding was utilized.
C. Ordinal label encoding was utilized.
D. Clustering was not utilized.

Answer: C

Explanation:
Ordinal encoding assigns numeric values in an order (e.g., Poodle = 1, Labrador = 2, Husky = 3).
This falsely implies hierarchy or ranking among breeds, introducing bias into the model.
AAIA emphasizes that incorrect encoding of categorical variables can cause:
Artificially weighted importance
Misleading correlations
Incorrect predictions based on false order

NEW QUESTION # 151
Which of the following is the MOST important reason to conduct regular threat modeling exercises for AI systems and data?

A. To prevent instances of AI model drift
B. To comply with AI regulatory requirements
C. To proactively identify potential vulnerabilities in AI systems
D. To assess the performance of AI algorithms

Answer: C

Explanation:
Regular threat modeling allows organizations to proactively identify vulnerabilities specific to AI, such as:
* Data poisoning
* Model inversion attacks
* Membership inference attacks
* Prompt injection
* Unauthorized model manipulationAAIA highlights AI threat modeling as essential for anticipating and mitigating AI-specific security risks that traditional IT threat modeling does not fully address.Option C (regulatory compliance) is a secondary benefit. Option D (drift prevention) is unrelated. Option B relates to performance, not security.The primary purpose is early identification of security weaknesses before exploitation occurs.
References:
AAIA Domain 2: Threats and Vulnerabilities Specific to AI.
AAIA Domain 1: Risk Assessment and Control Planning.

NEW QUESTION # 152
An organization is adopting AI for its procurement and inventory teams, raising concern from stakeholders that they will lose their jobs due to AI. Which of the following is the BEST way for the IS auditor to assess whether the potential negative impacts were minimized?

A. Review how the project management team collected feedback in engagement activities.
B. Review the AI roadmap for short-term and long-term milestones.
C. Review human-centered design practices to determine how they were considered.
D. Review the current state assessment of how AI may impact the organization.

Answer: C

NEW QUESTION # 153
An organization plans to implement an AI search and chatbot solution provided by an external vendor. Which of the following is MOST important for an IS auditor to confirm?

A. Whether the solution effectively enables web integration
B. Whether the organization or the vendor has the test plan for the solution
C. Whether the solution is tied to a bilingual evaluation understudy (BLEU) metric
D. Whether the vendor can provide an independent third-party attestation

Answer: D

Explanation:
For third-party (SaaS) AI solutions, the organization relies on the vendor's internal controls to ensure data security, privacy, and model integrity. The AAIATM manual highlights that the most critical confirmation is "Whether the vendor can provide an independent third-party attestation" (such as a SOC 2 Type II or ISO/IEC 42001 report). This provides the organization with reliable evidence that the vendor's AI governance and security practices have been verified by an external auditor.

NEW QUESTION # 154
An AI audit tool incorrectly flagged that business decisions were biased, leading to inappropriate management action plans. Which of the following can BEST prevent this risk?

A. Apply explainable AI validation methods.
B. Reweight groups to oversample.
C. Use different fairness definitions.
D. Document assumptions and record scope.

Answer: A

Explanation:
AI tools can produce " False Positives " in bias detection if they misinterpret the data. To prevent management from acting on incorrect audit findings, " Explainable AI (XAI) validation methods " (like SHAP or LIME) should be applied. XAI allows the auditor to see why the tool flagged a specific decision as biased.
If the tool ' s reasoning is flawed (e.g., it ignored a valid business justification), the auditor can correct the finding before it reaches management. This adds a necessary layer of " Auditor skepticism " and human validation to AI-driven audit insights.

NEW QUESTION # 155
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