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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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| Exam Duration: | 120 minutes |
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| Real Exam Qty: | 90 |
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| Related Certifications: | ACCA FCCA CISA CPA CIA |
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| Passing Score: | 65% |
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| Available Languages: | English |
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| Exam Format: | Multiple Choice, Computer-Based, Remotely Proctored |
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| Sample Questions: | ISACA AAIA Sample Questions |
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| Exam Way: | Online remotely proctored computer-based exam |
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| 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. |
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| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia |
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ISACA AAIA 考試大綱:
| 主題 | 簡介 |
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| 主題 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.
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| 主題 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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| 主題 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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最新的 Advanced in AI Audit AAIA 免費考試真題 (Q223-Q228):
問題 #223
Which of the following is the BEST way to test for proxy discrimination in a model that does not directly use a protected attribute (e.g., race)?
- A. Remove all numeric features
- B. Test whether other correlated features (e.g., zip code) act as proxies that reproduce the same disparate outcomes
- C. Reduce the number of training epochs
- D. Increase the model's regularization parameter
答案:B
解題說明:
Proxy discrimination occurs when non-protected features correlate strongly with a protected attribute, effectively reintroducing bias. Correlation and outcome analysis on such features helps detect this.
問題 #224
Which of the following is the PRIMARY benefit of implementing a robust data governance framework specific to AI solutions in an organization?
- A. It fosters adherence to industry regulations while minimizing the risk of data breaches and privacy violations.
- B. It accelerates AI implementation timelines by fully automating data preparation processes.
- C. It reduces the need for human oversight, ensuring seamless and autonomous data governance.
- D. It focuses on enhancing the accuracy and reliability of AI model predictions.
答案:A
解題說明:
According to the AAIA™ Study Guide, a robust data governance framework ensures that AI systems are compliant with data protection laws, ethical standards, and internal policies. It provides controls over data quality, access, retention, and processing, all of which are essential to avoid breaches and maintain trust.
"A strong data governance structure is foundational for regulatory compliance and ethical AI practices. It ensures that data privacy, integrity, and usage rights are maintained across the AI lifecycle." While option A is an outcome of good data governance, and automation (B) may improve efficiency, the most fundamental benefit is risk reduction and compliance (C). Option D reflects a misunderstanding of governance which requires human oversight.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Data Governance Frameworks and Compliance"
問題 #225
Which of the following is the MOST important reason for measuring the AI system's performance against predefined metrics in a staging environment?
- A. To ensure the AI system complies with privacy standards before production deployment
- B. To validate that the AI system meets requirements prior to release
- C. To confirm the AI system has been trained on the most recent dataset
- D. To verify that the AI system's user interface is aligned with accessibility standards
答案:B
解題說明:
The AAIATM Study Guide emphasizes the importance of a rigorous "Staging" or "User Acceptance Testing" (UAT) phase in the AI lifecycle. Measuring performance against predefined metrics (such as Precision, Recall, or F1-Score) in a non-production environment is critical to
"Validate that the system meets business and technical requirements prior to release". This step prevents the deployment of models that may exhibit bias, inaccuracy, or logic errors in the real world.
問題 #226
The GREATEST benefit of using AI auditing techniques over traditional methods is that AI auditing techniques can:
- A. significantly reduce data bias.
- B. identify complex data patterns.
- C. ensure full compliance with regulations.
- D. eliminate the need for human intervention.
答案:B
解題說明:
AI auditing techniques excel at identifying complex data patterns (option C), which is their primary advantage over manual or traditional audit approaches. The AAIA™ Study Guide states, "AI-based audit tools can process massive volumes of data at speed and depth, detecting anomalies, trends, or relationships that might be invisible to human auditors or unfeasible to uncover manually." AI does not fully eliminate the need for human involvement, nor does it guarantee compliance or the elimination of bias, but it can analyze intricate patterns in large, multidimensional data sets.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Advantages of AI-Enabled Audit Approaches"
問題 #227
An organization utilizes an off-the-shelf generative AI solution for internal business processing. According to the principle of shared responsibility, which of the following areas remains the PRIMARY responsibility of the organization?
- A. Ensuring the model ' s core safety and security systems are robustly designed
- B. Defining appropriate usage policies and enforcing identity and access management (IAM)
- C. Maintaining the underlying AI compute infrastructure
- D. Conducting initial training of the base foundation model
答案:B
解題說明:
In a " Software as a Service " (SaaS) or off-the-shelf AI model deployment, the " Shared Responsibility Model " dictates that the vendor is responsible for the " Security OF the model " (infrastructure, base training, core safety), while the customer is responsible for the " Security IN the model " (usage). The organization ' s primary duty is " Defining usage policies and enforcing IAM " to ensure that only authorized employees use the tool and that they do not input sensitive corporate data into unvetted prompts. This governance ensures the AI is used ethically and securely within the organization's specific operational context.
問題 #228
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