AAIA PDF問題サンプルはISACA Advanced in AI Auditに合格するのに便利なキーです

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ISACA AAIA 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 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.
トピック 3	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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ISACA Advanced in AI Audit 認定 AAIA 試験問題 (Q227-Q232):

質問 # 227
An organization is evaluating whether to classify a new AI use case as "high-risk" under its internal AI risk framework. Which factor is MOST relevant to this determination?

A. The potential impact on individuals' rights, safety, or significant life opportunities (e.g., employment, credit, healthcare)
B. The size of the development team
C. The cloud provider hosting the model
D. The programming language used

正解：A

解説：
Risk classification frameworks (including the EU AI Act's approach) generally tier risk based on the potential impact of the AI system's decisions on individuals, not on technical implementation details.

質問 # 228
Which of the following BEST supports reproducibility of AI model results during an audit?

A. Version control of code, data, and model parameters
B. A well-written user manual
C. A large training dataset
D. High model accuracy

正解：A

解説：
Reproducibility requires that the exact code, data snapshot, hyperparameters, and environment used to produce a result can be retrieved and rerun -- this is achieved through rigorous versioning practices (MLOps).

質問 # 229
A financial institution's customer service chatbot sometimes gives users incorrect legal advice, leading to several customer complaints and potential regulatory exposure. Which of the following should be done FIRST to mitigate this risk?

A. Expand the chatbot's training dataset.
B. Introduce a human-in-the-loop review process.
C. Review chatbot technical documentation.
D. Retrain the chatbot on a more diverse dataset.

正解：B

解説：
When a customer-facing system is providing harmful or incorrect advice (particularly legal or financial), the "Immediate Priority" is to prevent further harm. "Introducing a human-in-the-loop review process" ensures that a qualified human checks and approves the chatbot's advice before it is sent to the customer. This acts as an immediate safety "guardrail."

質問 # 230
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. The service level agreement (SLA) does not include network latency and inference guarantees
B. Cloud provider employees have limited AI skills
C. AI model audit trails have not been comprehensively documented
D. The cloud provider has not adopted an ethical AI governance framework

正解：A

解説：
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

質問 # 231
Which of the following is the MOST important task when gathering data during the AI system development process?

A. Isolating the system
B. Cleaning the data
C. Training the system
D. Stratifying the data

正解：B

質問 # 232
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