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ISACA AAIA Exam Overview:

Certification Vendor:ISACA
Exam Name:ISACA Advanced in AI Audit
Exam Number:AAIA
Available Languages:English
Exam Duration:120 minutes
Related Certifications:ACCA
FCCA
CIA
CPA
CISA
Passing Score:65%
Real Exam Qty:90
Exam Format:Computer-Based, Remotely Proctored, Multiple Choice
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
  • 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.
Topic 3
  • 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.

ISACA Advanced in AI Audit Sample Questions (Q194-Q199):

NEW QUESTION # 194
When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?

Answer: B

Explanation:
Natural Language Processing (NLP) enables systems to understand, summarize, and extract relevant insights from unstructured text data such as contracts, emails, or reports. The AAIA™ Study Guide recognizes NLP as the optimal tool for document review during audit planning or risk assessment phases.
"NLP techniques can efficiently process large volumes of documentation, highlight key clauses, and identify inconsistencies or risk indicators. This enhances audit productivity and thoroughness." While RPA (A) automates repetitive tasks, it doesn't interpret content. Predictive analytics (C) forecasts trends, and autoregressive models (B) generate sequences, not summarize documents.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "NLP Applications in Audit Planning and Risk Identification"


NEW QUESTION # 195
An IS auditor examining change management procedures for an AI system observes inconsistent training data validation and verification protocols prior to model retraining. Which of the following is the MOST significant risk in this context?

Answer: C

Explanation:
When training data validation is inconsistent, the most severe risk is that the AI model may learn from incorrect, incomplete, biased, or corrupted data. This directly leads to a degradation of system reliability (option C), which manifests as inaccurate predictions, higher error rates, bias, or unstable behavior.
AAIA emphasizes that data validation prior to retraining is one of the most important controls because model behavior is fully dependent on training data integrity. If the quality and correctness of the data cannot be guaranteed, the resulting model outputs become unreliable, which can undermine compliance, operational decisions, and user trust.


NEW QUESTION # 196
An IS auditor observes that an AI-based fraud detection system used by an insurance organization produces inconsistent outcomes when processing similar cases. Which of the following is the auditor ' s MOST efficient recommendation?

Answer: A

Explanation:
Inconsistent outcomes for similar cases often indicate issues with " Model Stability " or " Data Quality " . The most efficient recommendation is to " Analyze the training data and model evaluation parameters " to identify if the model was trained on biased, noisy, or non-representative datasets. Inconsistencies may also stem from poorly tuned hyperparameters that cause the model to react erratically to minor variations in input. While a human-in-the-loop (Option A) provides a safeguard, it is resource-intensive and does not address the root cause. Investigating the data and parameters allows the organization to fix the underlying logic of the model for long-term consistency.


NEW QUESTION # 197
Which of the following is an IS auditor's MOST important course of action when determining whether source data should be entered into approved generative AI tools to assist with an audit?

Answer: B

Explanation:
When using generative AI tools during audit activities, the most critical concern is the reliability and appropriateness of the information being entered and processed. According to the AAIATM Study Guide, auditors are accountable for ensuring that audit data is valid, confidential, and that generated outputs are factual and verifiable.
"IS auditors must evaluate whether the information entered into AI tools is reliable and appropriate for the audit context. Inputting sensitive or unverified data may lead to regulatory violations or audit inaccuracies."


NEW QUESTION # 198
Which of the following is the GREATEST risk associated with the use of AI coding tools by software developers?

Answer: B

Explanation:
AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.
AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:
* Data breaches
* Injection attacks
* Authentication bypasses
* Supply chain compromise
* Privilege escalation
Excessive reliance (A) affects productivity but not security.
Human bias (B) is possible but not as severe as security flaws.
Training complexity (D) is not a risk to AI system integrity.
Therefore, the introduction of security vulnerabilities is the greatest risk.
References:
AAIA Domain 2: AI Risks in Software Development
AAIA Domain 5: Security and Ethical Development Practices


NEW QUESTION # 199
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