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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit
Exam Number:	AAIA
Exam Duration:	150 minutes
Available Languages:	Chinese, English, Spanish
Real Exam Qty:	90
Certificate Validity Period:	3 years
Exam Format:	Scenario-based questions, Multiple-choice questions, Computer-based testing
Passing Score:	450 (scaled, range 200–800)
Exam Price:	US$459 (member) / US$599 (non-member)
Related Certifications:	ACCA CISA CPA CIA FCCA
Recommended Training:	AAIA Review Manual AAIA Official Review Course
Exam Registration:	ISACA Official Registration
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong)
Pre Condition:	Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline

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ISACA AAIA Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 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 AAIA Prüfungsfragen mit Lösungen (Q69-Q74):

69. Frage
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 protect data privacy
C. Controls to prevent data exfiltration
D. Controls to manage data governance

Antwort: A

Begründung:
AI systems face unique threats not commonly found in traditional IT environments, particularly data 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 of AI- specific vulnerabilities.

70. Frage
Which of the following is MOST important to consider when auditing an organization's AI procedures?

A. AI data validation and filtration to prevent data poisoning
B. Backup and recovery in the event of an AI data breach
C. Frequency of AI system updates to enhance security
D. Employee training on recognized AI best practices

Antwort: A

Begründung:
The integrity of data fed into AI systems is a critical concern. The AAIA™ Study Guide emphasizes that validation and filtration processes are essential to mitigate the risk of data poisoning-an attack that can manipulate model behavior by injecting malicious inputs.
"Data poisoning represents a major vulnerability in AI pipelines. Effective controls include robust validation, filtration, and monitoring of training data sources. These preventive practices are essential to ensure model reliability and security." While options A, B, and C are important operational and training measures, only D addresses a technical risk that can directly compromise model outputs and trustworthiness.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "AI Data Integrity and Attack Prevention"

71. Frage
Which of the following represents the PRIMARY benefit of reviewing model cards during AI model acquisition and risk assessment?

A. Establishing legally binding contracts for model use
B. Complying with documentation requirements
C. Understanding model intent and performance context
D. Identifying model vulnerabilities and training needs

Antwort: C

Begründung:
A " Model Card " is a standardized document that provides essential information about an AI model's intended use, training data, limitations, and performance metrics. For an auditor or risk manager, the primary benefit is gaining a clear " Understanding of model intent and performance context. " It allows the organization to determine if a vendor ' s model is fit for the specific business purpose and to identify potential risks (such as data bias or environmental limitations) before acquisition. While it supports documentation compliance (Option A), its core value lies in providing the transparency necessary for informed decision- making and governance.

72. Frage
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?

A. Delays in AI model retraining due to procedural inefficiencies
B. Degradation of system reliability due to compromised or substandard data
C. Noncompliance due to inadequate model training documentation
D. Addition of AI model complexity due to inconsistent data inputs

Antwort: B

Begründung:
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.

73. Frage
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?

A. Validate that the tool provides a privacy notice.
B. Validate that the tool is leveraging the latest model.
C. Determine whether the information is reliable.
D. Determine whether any AI model hallucinations have occurred.

Antwort: C

74. Frage
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