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CertifyCerts – Isaca AAIA Exam Details 2026
The Advanced in AI Audit (AAIA) certification by :contentReference[oaicite:0]{index=0} is the world’s first advanced AI audit certification specifically designed for experienced audit and advisory professionals. The certification validates expertise in AI governance, AI operational controls, AI auditing methodologies, compliance, risk management, and responsible AI oversight. In 2026, AAIA has become one of the most in-demand AI governance certifications for IT auditors, compliance leaders, and risk professionals.
| Exam Feature | Details |
|---|---|
| Certification Name | Advanced in AI Audit (AAIA) |
| Exam Code | AAIA |
| Certification Provider | ISACA |
| Exam Format | Multiple Choice Questions (MCQs) & Scenario-Based Questions |
| Total Questions | 90 Questions |
| Exam Duration | 150 Minutes |
| Passing Score | Scaled Scoring System |
| Exam Cost (ISACA Members) | USD $459 |
| Exam Cost (Non-Members) | USD $599 |
| Application Processing Fee | USD $50 |
| Difficulty Level | Advanced |
| Exam Delivery | Remote Proctored & PSI Testing Centers |
| Certification Validity | Annual Maintenance with CPE Requirements |
| Eligibility Requirement | Active CISA or Approved Advanced Auditing Certification Holder |
| Recommended Experience | AI governance, IT auditing, cybersecurity, enterprise risk, compliance, privacy, and AI operational oversight |
| Target Audience | IT Auditors, Internal Auditors, Risk Professionals, Compliance Managers, Governance Specialists, AI Assurance Consultants, Cybersecurity Auditors |
| Languages Available | English |
CertifyCerts – ISACA Advanced in AI Audit (AAIA) Exam Breakdown 2026
| Domain | Weight | Key Topics |
|---|---|---|
| AI Governance and Risk | 33% | AI Governance Frameworks, Responsible AI, AI Policies, Enterprise AI Risk, Data Governance, Privacy Controls, AI Ethics, Regulatory Compliance, AI Security Oversight |
| AI Operations | 34% | AI System Development Lifecycle, AI Deployment Controls, Model Validation, Operational Monitoring, Bias Management, AI Lifecycle Risk Management, AI Change Management |
| AI Auditing Tools and Techniques | 33% | AI Audit Planning, Audit Evidence Collection, AI Testing Procedures, Audit Reporting, Continuous Monitoring, Audit Analytics, AI Assurance Methodologies |
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Question # 1
An IS auditor identified data quality issues as a result of insufficient availability of minority data to address diverse class representation. Which of the following is the BEST recommendation to resolve this issue?
A. Augment the data through synthesizing.
B. Change the model selection. 3. Document the risk acceptance.
C. Relabel available data.
Question # 2
Which control is MOST important to verify in order to ensure proper data management with AI systems?
A. A data governance framework has been established.
B. Appropriate data metrics have been developed.
C. A data inventory has been determined.
D. Data has been labeled according to sensitivity.
Question # 3
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?
A. Lack of transparency in AI algorithms
B. Narrowly targeted AI awareness training
C. Inadequate documentation for AI tools
D. Use of AI to automate routine manual tasks
Question # 4
An IS auditor detected a "Prompt Injection" embedded in an email from a vendor that used an invisible font to hide text. Which of the following is the BEST control?
A. Lower the temperature so the AI model is less likely to follow injections.
B. Add more instructions to the AI model about ignoring invisible text.
C. Implement text sanitization that removes invisible text before ingestion.
D. Implement text sanitization that changes fonts to default.
Question # 5
Which of the following is the BEST approach to mitigate the risk of AI model degradation?
A. Ensuring models are trained on both model-generated and human-generated data
B. Introducing continuous monitoring to identify adversarial inputs
C. Increase the use of synthetic data for model training purposes
D. Implementing periodic human reviews of model outputs and recommendations
Question # 6
An organization deploys an AI-based image recognition system that is vulnerable to evasion attacks. Which of the following approaches BEST helps to ensure the system mitigates these evasion attempts?
A. Static rule-based input filtering and validation
B. Stringent model accuracy evaluation criteria
C. Implementation of model ensembles
D. Adversarial training and regular bias reviews
Question # 7
Which of the following is the PRIMARY benefit of using linear regression in machine learning (ML) when transparency and auditability are required?
A. Ease of interpretation
B. Avoidance of overfitting
C. Reduction of model bias
D. Predictive accuracy
Question # 8
Which of the following sampling strategies would MOST likely involve the use of AI?
A. Adaptive
B. Statistical
C. Systematic
D. Judgmental
Question # 9
Which of the following is MOST important to perform when testing an AI model for security?
A. Regression testing
B. Model retraining
C. Vulnerability assessments
D. Data validation
Question # 10
Which of the following is the GREATEST challenge when auditing an AI-based decisionmaking system?
A. Accounting for deterministic AI model results
B. Explaining sequential algorithmic rules
C. Understanding the transparency of AI models
D. Managing large datasets
Question # 11
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?
A. Model extraction
B. Model poisoning
C. Model inversion
D. Model drift
Question # 12
A retailer's AI pricing engine recommends unusually large discounts in certain regions. An audit reveals that most historical sales data used for model training came from large urban areas, while smaller regions were represented by very few or no sales records. Which of the following is the MOST likely cause of this issue?
A. Labeling inconsistencies in regional identifiers
B. Data scarcity in the training set that causes the model to be unable to learn regional patterns
C. Model drift from recent changes in customer demand
D. Overfitting of the model resulting in misrepresentation of sales records
Question # 13
An organization has exhausted its internal data sources to train an AI model. Which of the following is the BEST source to obtain new data?
A. Data from web scraping
B. Copyright-free data
C. Extended retention of internal data
D. Shadow data
Question # 14
An IS auditor analyzed an AI model scorecard and identified that training data was imbalanced. Which of the following is the BEST recommendation to remediate risk?
A. Use class-weighted loss with stratified train/validation splits.
B. Tune decision thresholds using expected misclassification costs.
C. Optimize the decision threshold for maximum F1 on validation.
D. Increase the capacity of the model with early stopping.
Question # 15
An organization implements a neural network-based AI system for credit scoring that processes extensive input data from multiple sources. Which of the following represents the GREATEST risk if input validation and anomaly detection controls are inadequate?
A. Biased inputs that can affect credit decisions
B. Overfitting caused by excessive anomaly filtering
C. Increased computational cost due to redundant input checks
D. Delayed model training cycles from excessive data preprocessing
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