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ISACA AAIA Exam Overview:
| Certification Vendor: | ISACA |
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| Exam Name: | ISACA Advanced in AI Audit |
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| Exam Number: | AAIA |
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| Certificate Validity Period: | 3 years |
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| Real Exam Qty: | 90 |
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| Exam Duration: | 150 minutes |
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| Exam Format: | Computer-based testing, Multiple-choice questions, Scenario-based questions |
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| Passing Score: | 450 (scaled, range 200–800) |
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| Available Languages: | Chinese, Spanish, English |
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| Exam Price: | US$459 (member) / US$599 (non-member) |
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| Related Certifications: | CISA FCCA ACCA CPA CIA |
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| Recommended Training: | AAIA Official Review Course AAIA Review Manual |
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| Exam Registration: | ISACA Official Registration |
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| Sample Questions: | ISACA AAIA Sample Questions |
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| Exam Way: | Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong) |
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| Pre Condition: | Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus |
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| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline |
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| Topic | Details |
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| 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.
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| 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.
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| 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 (Q89-Q94):
NEW QUESTION # 89
Which of the following should be applied to an AI system but are not typically used in traditional systems?
- A. Controls to monitor data poisoning
- B. Controls to manage data governance
- C. Controls to prevent data exfiltration
- D. Controls to protect data privacy
Answer: A
Explanation:
AI systems faceunique threatsnot commonly found in traditional IT environments, particularlydata 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 ofAI-specific vulnerabilities.
While privacy (A), data exfiltration (C), and data governance (D) controls are essential for all digital systems, monitoring for data poisoningis uniquely critical for AI because poisoned inputs can lead to faulty predictions, safety issues, or systemic bias. AAIA specifically highlights data poisoning as a distinct threat requiring specialized controls.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Threats and Vulnerabilities Specific to AI.
ISACA AI security guidance discussing poisoning and integrity attacks.
NEW QUESTION # 90
Which of the following controls would MOST effectively mitigate worst-case service disruption scenarios affecting an AI-based application system?
- A. Implementing a kill chain process in the event of disruption
- B. Performing periodic tabletop exercises
- C. Including a range of AI disruption scenarios in the disaster recovery plan (DRP)
- D. Updating key risk indicators (KRIs) regularly
Answer: C
Explanation:
Including AI-specific disruption scenarios in the organization's Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIATM Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.
"AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift. Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime."
NEW QUESTION # 91
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?
- A. Hiring a marketing firm to text links to customers requesting product reviews for monetary compensation
- B. Augmenting the unbalanced product review data set with the use of oversampling by the model developer
- C. Requiring customers to authenticate access to their accounts prior to writing product reviews
- D. Peer reviewing code that acquires product reviews from social media posts
Answer: C
Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."
NEW QUESTION # 92
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?
- A. Limitations can arise in the quantification of risk profiles.
- B. Computational logic is based on probabilities.
- C. Neural node access varies each time the process is executed.
- D. Servers are reconfigured periodically.
Answer: B
Explanation:
Generative AI systems, particularly those based on transformer models, produce outputs using probabilistic computations. As a result, even when given the same input data, these models may generate different outputs depending on sampling strategies (e.g., temperature, top-k sampling).
"Generative AI operates probabilistically, meaning that outputs can vary with each run based on stochastic sampling techniques. This variability is expected and must be accounted for in risk- sensitive environments like finance."
NEW QUESTION # 93
Which of the following is the MOST important consideration when auditing an organization's process for decommissioning an AI model?
- A. Ensuring the model's code repository is deleted immediately
- B. Ensuring the model is replaced with a more expensive alternative
- C. Ensuring the development team receives a performance bonus
- D. Ensuring all related data, access, and dependencies are properly retired, and that historical records are retained per policy for accountability
Answer: D
Explanation:
Proper decommissioning requires structured retirement of data access, dependent systems, and integrations, while retaining sufficient records (per retention policy) to support future audits, investigations, or regulatory inquiries.
NEW QUESTION # 94
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