ISACA · AAIA
Validates the ability to audit AI systems across three domains: AI governance and risk management, AI operations and lifecycle risks, and AI auditing tools and techniques, covering AI model assessment, algorithm development oversight, and AI-enhanced audit processes.
Questions
600
Duration
150 minutes
Passing Score
450/800
Difficulty
AssociateLast Updated
Feb 2026
Use this AAIA practice exam to prepare for ISACA Advanced in AI Audit (AAIA) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for ISACA AAIA, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The ISACA Advanced in AI Audit™ (AAIA™) is the world's first advanced, audit-specific certification focused on artificial intelligence, launched by ISACA in 2025. It validates that experienced audit and assurance professionals possess the specialized knowledge to evaluate AI systems across three core disciplines: AI governance and risk management, AI operations and lifecycle management, and AI auditing tools and techniques. The credential demonstrates competency in assessing AI model integrity, overseeing algorithm development, applying data governance principles, and leveraging AI-enhanced methodologies to strengthen audit processes.
Designed for professionals who already hold a foundational audit or accounting credential, the AAIA goes beyond general AI literacy to test applied judgment in real-world scenarios—covering ethical AI frameworks, regulatory compliance, threat identification, incident response, and the use of AI-powered analytics within audit engagements. As organizations accelerate AI adoption, the certification equips auditors to serve as credible advisors on AI-related risk, control design, and assurance reporting.
The AAIA is intended for experienced IT auditors, internal auditors, and assurance advisors who already hold a qualifying credential such as the CISA, CIA, US CPA, ACCA/FCCA, Canadian CPA, CPA Australia, or Japanese CPA (JICPA). It is best suited for professionals with several years of audit or advisory experience who are now encountering AI systems in the scope of their work and need a recognized credential to formalize that expertise.
Beyond traditional IT audit roles, the certification is also relevant to risk managers, compliance officers, technology consultants, and governance professionals in industries such as financial services, healthcare, and government—anywhere that AI deployments require independent assurance and structured oversight.
Candidates must hold an active, in-good-standing qualifying credential from an approved list: CISA (ISACA), CIA (IIA), US CPA (AICPA), ACCA or FCCA (Association of Chartered Certified Accountants), Canadian CPA, CPA Australia (CPA or FCPA), or Japanese CPA (JICPA). There are no formal work-experience requirements beyond holding one of these designations, but the exam content presupposes familiarity with audit methodology, risk assessment frameworks, and IT controls.
ISACA recommends that candidates have practical experience conducting IT or operational audits before attempting the AAIA, as the questions are scenario-based and test applied judgment rather than rote knowledge. Candidates do not need a prior AI background, though familiarity with AI concepts, machine learning lifecycles, and data governance will significantly aid preparation.
The AAIA exam consists of 90 multiple-choice questions, each presenting four answer options. Candidates have 150 minutes to complete the exam. Questions are entirely scenario-based, requiring candidates to analyze situations and select the best course of action rather than recall definitions. There are no unscored pretest items disclosed publicly.
The exam is delivered via computer at authorized PSI testing centers worldwide or through live remote proctoring. Candidates residing in India, Mainland China, or Hong Kong must test at a PSI center and are not eligible for remote proctoring. Scoring uses a scaled system ranging from 200 to 800; the passing score is 450. Preliminary pass/fail status is displayed on screen immediately after completion, and official scaled scores are emailed and posted to the candidate's ISACA account within 10 business days. Candidates who do not pass may retake up to four times within a 12-month period, with mandatory waiting periods of 30 days after the first failure and 90 days after subsequent failures.
The AAIA positions holders at the intersection of two high-demand disciplines—AI governance and professional audit—at a time when enterprises are rapidly scaling AI deployments while regulators worldwide (EU AI Act, SEC guidance, NIST AI RMF) are tightening accountability requirements. Certified professionals report salary premiums averaging 15–20% over non-certified peers in comparable audit roles, and the credential opens pathways to specialized positions including AI Audit Lead, Chief Risk Officer, AI Compliance Manager, and technology assurance advisory roles.
Because the AAIA is the only advanced, audit-specific AI credential in the market, it carries early-mover advantage: organizations in financial services, healthcare, government, and technology are actively seeking auditors who can independently assess AI risk without relying solely on data science teams. The certification is globally recognized and maintains the ISACA brand's credibility with audit committees and regulators, making it a strong differentiator when competing for senior internal audit, consulting, or advisory mandates involving AI systems.
5 sample questions with answers and explanations. Start a practice session to test yourself across all 600 questions.
Preview — answers shown1. An auditor evaluates EU AI Act Article 10 data governance compliance for a recruitment AI system deployed across 12 EU member states. The system uses historical hiring data from 2015-2023. Analysis reveals the training dataset contains 68% male candidates for engineering positions, reflecting historical gender imbalances. The provider documented this imbalance but took no corrective action. Which Article 10 requirement has been violated? (Select one!)
Explanation
EU AI Act Article 10 explicitly requires that data sets contain accurate information and potential bias must be identified and mitigated. The organization identified the gender imbalance bias but failed to implement mitigation measures such as reweighting, resampling, or algorithmic fairness constraints. Documentation alone does not satisfy the mitigation requirement. The data may meet accuracy and completeness standards while still containing bias. The scenario does not indicate documentation deficiencies or dataset separation issues. For high-risk employment systems under the EU AI Act, bias identification triggers mandatory mitigation obligations.
2. An auditor is assessing an organization's AI system categorization under the NIST AI RMF MAP function. The organization has developed an AI-powered legal document analysis system that summarizes contracts and identifies potential compliance risks. The system is categorized as low-risk with minimal governance oversight. The system operates in the legal domain, makes recommendations affecting business decisions, and occasionally misinterprets complex legal language leading to missed compliance issues. Which MAP subcategory has the organization most likely failed to properly implement? (Select one!)
Explanation
The organization has failed to properly implement MAP 2.2 which requires characterizing knowledge limits and scientific integrity of the AI system. The key indicators are that the system occasionally misinterprets complex legal language and misses compliance issues, yet is categorized as low-risk. MAP 2.2 specifically addresses understanding where AI systems lack knowledge or capabilities, which is critical for legal document analysis where nuanced interpretation matters. The system's inability to reliably handle complex legal language represents a knowledge limit that should elevate risk categorization and trigger stronger governance. While MAP 1.1 addresses intended use documentation, the issue is not missing documentation but incorrect risk categorization despite known limitations. MAP 3.1 involves benefits versus costs analysis, but the core problem is not ROI evaluation but rather failure to recognize technical limitations. MAP 5.1 addresses stakeholder engagement for impact characterization, but the primary failure is not gathering stakeholder input but rather failing to acknowledge and document the system's technical boundaries in legal interpretation. Proper MAP 2.2 implementation would have identified these knowledge limits and resulted in higher risk categorization with appropriate oversight.
3. An auditor reviews ISO/IEC 42001 Clause 9.2 internal audit implementation for an AI management system. The organization conducts annual IT audits covering general technology controls and includes AI systems within scope but does not evaluate AI-specific controls such as bias monitoring, model performance tracking, or AI impact assessments. The audit manager states that AI systems are covered under existing IT audit procedures. What is the deficiency? (Select one!)
Explanation
ISO/IEC 42001 Clause 9.2 requires internal audits to provide assurance that the AI management system conforms to the standard's requirements and is effectively implemented. This necessitates evaluating AI-specific controls including bias monitoring, model performance tracking, impact assessments, and AI lifecycle management beyond general IT controls. Simply including AI systems under existing IT audit scope without addressing AI-specific requirements does not fulfill Clause 9.2 obligations. ISO 42001 does not mandate quarterly audit frequency; organizations determine appropriate intervals based on risk and system maturity. Clause 9.2 applies to organizations implementing the standard regardless of formal certification status. AI-specific controls are within internal audit scope when auditing an AI management system; distinguishing operational from audit responsibilities does not exempt these controls from evaluation.
4. An e-commerce company implements SHAP (SHapley Additive exPlanations) for explaining product recommendation model predictions. For a specific customer recommendation, SHAP values show: previous_purchases (+2.3), browsing_history (+1.8), time_of_day (-0.5), customer_age (+0.3), with baseline prediction of 0.2 and final prediction of 4.1. The customer's lawyer challenges the recommendation as discriminatory, claiming age was used inappropriately. How should the auditor interpret these SHAP values? (Select one!)
Explanation
SHAP additivity property ensures feature contributions sum to the difference between prediction and baseline: 2.3 + 1.8 - 0.5 + 0.3 = 3.9, and baseline 0.2 + 3.9 = 4.1 matches final prediction, validating the explanation's mathematical correctness. However, mathematical validation does not equal legal compliance. Age is a protected characteristic in many jurisdictions; even small contributions require legal assessment of whether use is proportionate, necessary, and non-discriminatory. The fact that age contributed positively requires evaluation, not dismissal. Simply using age is not automatically discriminatory per se without context. Time_of_day is not a protected characteristic. Magnitude alone does not determine discrimination; even small age contributions can be problematic depending on context and jurisdiction.
5. A logistics company deploys machine learning models using CRISP-ML(Q) methodology. During Phase 4 ML Model Evaluation, the team performs offline accuracy testing and generates confusion matrices but does not conduct robustness assessment or explainability analysis before proceeding to deployment. The deployment fails when the model encounters data slightly different from training distributions. Which Phase 4 quality focus was neglected? (Select one!)
Explanation
CRISP-ML(Q) Phase 4 ML Model Evaluation requires performance validation, robustness assessment, and explainability testing. The team performed offline testing but neglected robustness assessment that would have revealed the model's inability to handle distribution shifts. This directly caused the deployment failure when encountering slightly different data. Performance validation was completed through accuracy testing. Metadata collection is Phase 3 ML Model Engineering, not Phase 4 evaluation. User acceptance testing is Phase 5 Deployment. The critical gap was the missing robustness assessment examining model behavior under varying conditions.
Certified Information Security Manager (CISM)
CISM · 1196 questions
Certified Information Systems Auditor (CISA)
CISA · 895 questions
Certified in Risk and Information Systems Control (CRISC)
CRISC · 761 questions
Certified Data Privacy Solutions Engineer (CDPSE)
CDPSE · 749 questions
IoT Fundamentals Certificate
IoT-Fund · 630 questions
IT Audit Fundamentals Certificate
IT-Audit-Fund · 627 questions
$17.99
One-time access to this exam