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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Real Exam Qty:90
Exam Format:Remotely Proctored, Multiple Choice, Computer-Based
Related Certifications:ACCA
CIA
CPA
FCCA
CISA
Passing Score:65%
Available Languages:English
Exam Duration:120 minutes
Sample Questions:ISACA AAIA Sample Questions
Exam Way:Online remotely proctored computer-based exam
Pre Condition:Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA.
Official Syllabus URL:https://www.isaca.org/credentialing/aaia

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ISACA AAIA Exam Syllabus Topics:

TopicDetails
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
  • 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 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.

ISACA Advanced in AI Audit Sample Questions (Q151-Q156):

NEW QUESTION # 151
Which of the following is the BEST approach for auditing an AI system's compliance with the
"right to explanation" under applicable privacy regulations?

Answer: B

Explanation:
The right to explanation (e.g., under GDPR Article 22) requires organizations to be able to provide individuals with meaningful information about automated decisions affecting them -- auditors should test whether this capability actually exists and functions.


NEW QUESTION # 152
Which of the following should be applied to an AI system but are not typically used in traditional systems?

Answer: B

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 # 153
An IS auditor is interviewing management about implemented controls around machine learning (ML) models deployed in the production environment. Which of the following schedules for reviewing the performance of a deployed model would be of GREATEST concern to the auditor?

Answer: A

Explanation:
Only reviewing an ML model's performance one time prior to migrating to production (option C) is of greatest concern. The AAIA™ Study Guide emphasizes that "AI and ML models require continuous monitoring and periodic performance reviews in production to detect issues such as data drift, model degradation, or evolving risk factors." A single pre-production review fails to capture these changes and risks, potentially resulting in undetected failures or compliance issues.
Periodic (including annual) and event-driven reviews are necessary to ensure ongoing model reliability.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Continuous Monitoring and Review of Deployed AI Models"


NEW QUESTION # 154
Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?

Answer: A

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 # 155
An organization seeks to sustain effective AI governance and risk management amid rapidly evolving AI technologies. Which of the following represents the MOST effective course of action?

Answer: D

Explanation:
Sustaining effective AI governance and risk management requires continuous, organization-wide awareness
, not just one-off or role-limited training. Option D embeds AI topics (governance, risk, ethics, privacy, and security) into the existing security awareness program , which is already a recurring and mandatory mechanism across the enterprise. This supports ongoing adaptation to rapidly evolving AI technologies, aligns with ISACA's emphasis on integrating AI risk considerations into existing governance and risk frameworks, and ensures that staff at all levels understand their responsibilities.
Options A and C are too narrow in scope, as they target only technical staff or senior management; they help but do not create pervasive, sustainable governance. Option B can supplement internal training, but outsourcing alone does not ensure continuity or alignment with internal policies.
References:
ISACA, AAIA Exam Content Outline - Domain 1: AI Governance and Risk (AI governance, AI training and awareness, program metrics).
ISACA, Advanced in AI Audit Exam Candidate Guide - sections on governance, risk, and professional responsibilities.


NEW QUESTION # 156
......

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