시험패스에유효한AAIA퍼펙트공부자료인증시험공부자료

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ISACA AAIA 시험요강:

주제	소개
주제 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.
주제 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.
주제 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.

>> AAIA퍼펙트 공부자료 <<

AAIA퍼펙트 공부자료 인기 인증시험은 덤프로 고고싱

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최신 Advanced in AI Audit AAIA 무료샘플문제 (Q14-Q19):

질문 # 14
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?

A. To ensure the systems align with the preservation of individual rights
B. To improve the accuracy and performance of the systems
C. To align AI system development with organizational values and principles
D. To identify and mitigate potential data drift within models

정답：A

설명：
The AAIATM Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.
"Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process. This is foundational in building public trust and avoiding legal liabilities." Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.

질문 # 15
Which of the following presents the GREATEST risk when an organization deploys a machine learning model in a public cloud environment for real-time predictions?

A. Cloud provider employees have limited AI skills
B. The service level agreement (SLA) does not include network latency and inference guarantees
C. The cloud provider has not adopted an ethical AI governance framework
D. AI model audit trails have not been comprehensively documented

정답：B

설명：
In a real-time prediction environment (e.g., fraud detection, medical triage, automotive risk), latency and inference speed directly affect safety, accuracy, and business performance.
If the SLA does not include guarantees for latency, the model may fail to deliver predictions in time, leading to:
Incorrect or delayed decisions
Transaction failures
Safety incidents in time-sensitive use cases
Compliance violations in regulated domains

질문 # 16
An IS auditor is auditing an AI system that predicts inventory needs. The system recently failed to predict a stock outage for a key product. Which of the following audit tests would BEST validate the system's accuracy?

A. Unit testing of the forecasting algorithm
B. Historical testing with past sales data
C. Load testing during peak sales periods
D. Sensitivity analysis on input variables

정답：B

설명：
The best way to validate the accuracy of a predictive AI system is to use historical testing with past sales data (option D). According to the AAIA™ Study Guide, "historical (or back-testing) is essential for evaluating how well a model would have performed using actual data from previous periods, directly reflecting its predictive validity." This method reveals any gaps or biases in the model by comparing predictions to known outcomes.
Unit testing, load testing, and sensitivity analysis are useful for technical verification and robustness but do not provide direct evidence of prediction accuracy in real-world scenarios.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Validation Techniques"

질문 # 17
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model's poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?

A. Comparative language evaluations with parity thresholds
B. Human review for non-English languages after go-live
C. Weighted metric with higher accuracy targets
D. Translate all outputs to English and evaluate in English for consistency

정답：A

설명：
A common pitfall in AI performance is "aggregate accuracy," which masks poor performance in specific demographics or subgroups. To detect these "subgroup regressions," auditors should look for "comparative evaluations" where the model is tested separately for different languages.
"Parity thresholds" establish the maximum allowable difference in performance between the majority group (English) and minority groups. This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.

질문 # 18
An organization is evaluating change management practices for AI-based decision support models. Which of the following BEST demonstrates effective AI-focused change management?

A. Deploying two separate copies of the model after each adjustment to compare results
B. Assigning a single data science team member to adjust the model in order to establish accountability
C. Engaging an independent expert to review the model's accuracy and precision on a quarterly basis
D. Documenting model updates and retraining sessions to ensure traceability

정답：D

설명：
Documenting model updates and retraining sessions to ensure traceability (option C) is the hallmark of mature, audit-ready change management in AI. The AAIATM Study Guide states,
"Traceability and documentation of all changes--including retraining events, parameter adjustments, and update rationales--enable effective audit trails, accountability, and regulatory compliance for AI systems." While reviews and comparisons are useful, only comprehensive documentation guarantees ongoing transparency and effective management.

질문 # 19
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