Advanced in AI Audit™
Practice Tests & Exam Prep
251 questions across all 3 official AAIA domains, researched against real-world AI governance and audit scenarios. The first comprehensive AAIA prep platform — start free today.
What Is the AAIA?
The ISACA Advanced in AI Audit™ (AAIA™) is the premier certification for professionals who audit, assess, and govern artificial intelligence systems. It validates your ability to advise on AI governance, assess AI operational risks, and apply AI-enabled audit tools and techniques.
AAIA is built for IS auditors, risk professionals, compliance officers, and governance leaders who need to stay ahead of rapidly evolving AI adoption risks across their organizations.
Exam Domains
Advising stakeholders on AI governance, ethical AI policy, data privacy, and risk mitigation including leading regulatory practices.
Assessing AI risk profiles, operational readiness, change management, AI solution testing, threat response, and incident management.
Audit planning, testing methodologies, evidence collection, data analytics, and AI-enabled audit reporting techniques.
About the Exam
Who Should Pursue AAIA?
Sample Practice Questions
AAIA questions are scenario-based and test your judgment on real AI audit situations.
An organization is implementing an AI-powered hiring tool. The internal auditor identifies that the model disproportionately rejects applications from certain demographic groups. Which risk BEST describes this finding?
Correct: B. Algorithmic bias occurs when an AI model produces systematically unfair outputs affecting protected groups. This is a core AI governance and ethics risk that auditors must identify and escalate. It also carries significant legal and reputational exposure.
During an AI audit, the auditor finds that the organization has no documented process for monitoring AI model drift. What is the PRIMARY risk this control gap creates?
Correct: C. Model drift occurs when an AI model's real-world performance degrades because input data patterns change after deployment. Without monitoring, the organization cannot detect when outputs become unreliable, creating operational and compliance risk.